JPH0915172A - Method and equipment for inspecting tire - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a method and equipment for inspecting a tire which enable shortening of an inspecting time and also execution of highly accurate inspection. SOLUTION: A plurality of image data picked up at the same image pickup position by an X-ray camera 22 are emphasized and added up by a preprocessing part 12 and then averaged by dividing the result of the addition by the number of times of the addition. In the main body 10 of equipment, the image data are subjected to a prescribed gray level transform processing and also a prescribed filtering processing and further to binarization, and the binary-coded image data are compared with prescribed reference data. Thereby the disposition of two breakers provided between a carcass and a tread of a tire and having a plurality of cords provided as attachments with prescribed spacing in different directions from each other is inspected and the quality of an internal structure is determined. According to this method, a result of higher accuracy than the one of human visual inspection and determination can be obtained and an inspecting operation can be automated with ease.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a tire inspection method and an apparatus therefor, and in particular, it is based on image data obtained by imaging the arrangement of two breakers provided between a carcass and a tread using electromagnetic radiation. The present invention relates to a tire inspection method and apparatus for inspecting the internal structure of a tire.

[0002]

2. Description of the Related Art As shown in FIG. 2, a tire 1 is roughly divided into four parts, a tread 2, a carcass 3, a breaker 4 and a bead 5. As is well known, the tread 2 is a rubber layer that covers the outside of the carcass 3 and the breaker 4, and the sidewall portion 2 that is thin from the crown portion 2a corresponding to the thick ground contact portion to the shoulder portion 2b.
c, the rim cushion portion 2d, and protects the carcass 3 and the breaker 4 inside, strong against wear, cuts and impacts, and has a tread design according to the purpose of use.

The carcass 3 is an important part of the skeleton of the tire 1, and is made of cloth in which cord cloths of warp threads are alternately and diagonally overlapped with each other. Plays.

The bead 5 serves to prevent the tire 1 from losing its shape caused by the air pressure and the external force, and also to stabilize the tire 1 on the rim 6 so that the tire 1 does not shake during traveling.

The breaker 4 is a layer interposed between the car cast 3 and the tread 2 to absorb the impact force of the tire 1 received from the outside and prevent cracks and external damages generated in the tread 2 from directly reaching the carcass 3. It also serves to prevent the tread 2 and the carcass 3 from peeling off.

This breaker 4 is, as shown in FIG.
Two pieces are provided, each made of a strip-shaped rubber cloth, and a large number of wire cords 4a are embedded in parallel in the breakers 4 in different oblique directions. As shown in FIG. 4, these breakers 4 are arranged so as to be slightly displaced in the width direction thereof, and are stuck on the carcass 3.

When inspecting the tire 1 having the above-mentioned structure, the arrangement of the breaker 4 and the condition inspection of the wire cord 4a are particularly important.

Conventionally, when inspecting the arrangement of the breaker 4 and the state of the wire cord 4a, an inspector visually inspects an image picked up by X-rays.

The normal layout of the breaker 4 is as shown in FIG. 4, but there are various things that are detected as abnormal. For example, there are 11 types shown in FIGS.

FIG. 5 shows a state in which two cloths forming the breaker 4 are lined up and overlapped.

In FIG. 6, the two cloths forming the breaker 4 are arranged in reverse order. That is, the direction of the wire cord 4a in the cloth is reversed.

In FIG. 7, the seams of the fabrics forming the breaker 4 are overlapped.

In FIG. 8, the seams of the cloth forming the breaker 4 are opened.

FIG. 9 shows a misalignment of the ends of the seams of the fabrics forming the breaker 4.

In FIG. 10, the end of the wire cord 4a in the breaker 4 is broken.

FIG. 11 shows that the edge of the cloth forming the breaker 4 is wavy and the position of the cloth changes to the left and right and is disturbed.

In FIG. 12, the ends of the wire cords 4a in the breaker 4 are separated.

In FIG. 13, the ends of the wire cords 4a in the breaker 4 are protruding.

In FIG. 14, the cloth forming the breaker 4 is defective, and the wire cord 4a and the rubber are folded and overlapped.

In FIG. 15, the foreign matter 7 is mixed in the breaker 4.

[0021]

As described above, conventionally, when the arrangement of the breaker 4 and the state of the wire cord 4a are inspected, the state is visually inspected by an inspector, which requires a lot of time for the inspection. At the same time, the inspection accuracy may decrease.

In view of the above problems, it is an object of the present invention to provide a tire inspection method and a tire inspection method capable of shortening the inspection time and performing highly accurate inspection.

[0023]

In order to achieve the above-mentioned object, the present invention provides, in claim 1, between a carcass and a tread of a tire, and a plurality of cords are provided side by side at predetermined intervals in different directions. A method of inspecting the internal structure of a tire based on image data obtained by imaging the arrangement of two breakers using electromagnetic radiation, after emphasizing the image data at the same imaging position. , After adding a plurality of image data for the same imaging position, dividing the addition result by the number of additions and averaging the image data, and performing a predetermined density conversion process on the image data. A predetermined filtering process is performed on the image data subjected to the density conversion process, the image data obtained by the filtering process is binarized, and the binarized image data and the predetermined image data are processed. Suggest tire testing method for determining the quality of the internal structure of the tire by comparing the quasi-data.

According to the tire inspection method, two breakers, which are provided between the carcass and the tread of the tire and have a plurality of cords arranged in different directions at predetermined intervals, are arranged by using electromagnetic radiation. The image data obtained by imaging is subjected to preprocessing. In this pre-processing, after the brightness of the image data at the same image pickup position is emphasized, a plurality of image data for the same image pickup position are added, and the addition result is divided by the number of additions to average the image data. As a result, the contrast is increased and the noise component is removed. After this pre-processing is performed, a predetermined density conversion process is performed on the image data, and a predetermined filter process is performed on the image data subjected to the density conversion process. Is further increased and the noise component is further removed. Further, the image data obtained by the filter processing is binarized, and the binarized image data is compared with predetermined reference data to judge the quality of the internal structure of the tire.

According to a second aspect of the present invention, in the tire inspection method according to the first aspect, the filtering process averages pixel densities in a predetermined region of the image data as a unit and binarizes the pixel concentration. We propose a tire inspection method for detecting the disorder of the end code of the breaker by comparing the image data with predetermined reference data.

According to the tire inspection method, two breakers, which are provided between the carcass and the tread of the tire and have a plurality of cords arranged in different directions at predetermined intervals, are arranged by using electromagnetic radiation. The image data obtained by imaging is subjected to preprocessing. In this pre-processing, after the brightness of the image data at the same image pickup position is emphasized, a plurality of image data for the same image pickup position are added, and the addition result is divided by the number of additions to average the image data. As a result, the contrast is increased and the noise component is removed. After this pre-processing is performed, a predetermined density conversion process is performed on the image data, and a predetermined filter process is performed on the image data subjected to the density conversion process. Is further increased and the noise component is further removed. In this filtering process, the pixel density is averaged within a predetermined area of the image data as a unit. Further, the image data obtained by the filter processing is binarized, the binarized image data is compared with predetermined reference data, and the disorder of the end code of the breaker is detected to detect the tire internal structure. The quality of is determined.

According to a third aspect of the present invention, in the tire inspection method according to the first aspect, the filtering process averages pixel densities within a predetermined region of the image data as a unit, and performs the binarization. We propose a tire inspection method for detecting jumping out of the end code of the breaker by comparing the image data with predetermined reference data.

According to the tire inspection method, two breakers, which are provided between the carcass and the tread of the tire and have a plurality of cords arranged in different directions at predetermined intervals, are arranged by using electromagnetic radiation. The image data obtained by imaging is subjected to preprocessing. In this preprocessing, after the brightness of the image data at the same image pickup position is emphasized, a plurality of image data for the same image pickup position are added, and the addition result is divided by the number of additions to average the image data. As a result, the contrast is increased and the noise component is removed. After performing this preprocessing, a predetermined density conversion process is performed on the image data, and a predetermined filter process is performed on the image data subjected to the density conversion process. In this filtering process, the pixel density is averaged within a predetermined area of the image data as a unit, and, for example, the contrast of the image is further increased and the noise component is further removed. Further, the image data obtained by the filtering process is binarized, the binarized image data is compared with predetermined reference data, and the protrusion of the end code of the breaker is detected to detect the tire internal structure. The quality of is determined.

According to a fourth aspect of the present invention, in the tire inspection method according to the first aspect, the filtering process averages pixel densities within a predetermined region of the image data as a unit and the predetermined region as a unit. As a result, a tire inspection method is proposed in which a differential process is performed within the region and the binarized image data is compared with predetermined reference data to detect the opening of the breaker seam.

According to the tire inspection method, two breakers, which are provided between the carcass and the tread of the tire and have a plurality of cords arranged in different directions at predetermined intervals, are arranged by using electromagnetic radiation. The image data obtained by imaging is subjected to preprocessing. In this pre-processing, after enhancing the brightness of the image data at the same image pickup position, a plurality of image data for the same image pickup position are added, and the addition result is divided by the number of additions to average the image data. As a result, the contrast is increased and the noise component is removed. After performing this preprocessing, a predetermined density conversion process is performed on the image data, and a predetermined filter process is performed on the image data subjected to the density conversion process. In this filter processing, the pixel density is averaged in a predetermined area of the image data as a unit, and the differentiation processing is performed in the predetermined area as a unit to further improve the contrast of the image. A noise component is further removed as the pixel density is increased, and a portion where the pixel density is largely changed is emphasized. Further, the image data obtained by the filter processing is binarized, the binarized image data is compared with predetermined reference data, and the opening of the breaker seam is detected to determine whether the tire internal structure is good or bad. Is determined.

According to a fifth aspect of the present invention, in the tire inspection method according to the first aspect, in the filtering process, a differentiation process is performed within a predetermined region of the image data as a unit, and the binarized image data is obtained. We propose a tire inspection method for detecting the overlap of the breaker seams by comparing with the predetermined reference data.

According to the tire inspection method, two breakers, which are provided between the carcass and the tread of the tire and have a plurality of cords arranged in different directions at predetermined intervals, are arranged by using electromagnetic radiation. The image data obtained by imaging is subjected to preprocessing. In this pre-processing, after enhancing the brightness of the image data at the same image pickup position, a plurality of image data for the same image pickup position are added, and the addition result is divided by the number of additions to average the image data. As a result, the contrast is increased and the noise component is removed. After performing this preprocessing, a predetermined density conversion process is performed on the image data, and a predetermined filter process is performed on the image data subjected to the density conversion process. In this filtering process, a differentiating process is performed in a predetermined region of the image data as a unit, and, for example, the contrast of the image is further increased, the noise component is further removed, and the pixel density is largely changed. The part is emphasized. Further, the image data obtained by the filter processing is binarized, the binarized image data is compared with predetermined reference data, the overlap of the breaker seam is detected, and the tire internal structure is good or bad. Is determined.

According to a sixth aspect of the present invention, in the tire inspection method according to the first aspect, in the filtering process, a differential process is performed in a predetermined region of the image data as a unit, and the binarized image data is obtained. And a predetermined reference data are compared to propose a tire inspection method for detecting breakage, breakage of the end code of the breaker or reverse arrangement of the two breakers.

According to the tire inspection method, two breakers, which are provided between the carcass and the tread of the tire and have a plurality of cords arranged in different directions at predetermined intervals, are arranged by using electromagnetic radiation. The image data obtained by imaging is subjected to preprocessing. In this pre-processing, after the brightness of the image data at the same image pickup position is emphasized, a plurality of image data for the same image pickup position are added, and the addition result is divided by the number of additions to average the image data. As a result, the contrast is increased and the noise component is removed. After performing this preprocessing, a predetermined density conversion process is performed on the image data, and a predetermined filter process is performed on the image data subjected to the density conversion process. In this filter processing, a differentiating process is performed within a predetermined area of the image data as a unit, and, for example, the contrast of the image is further increased and the noise component is further removed. Further, the image data obtained by the filter processing is binarized, the binarized image data is compared with predetermined reference data, and the breaker end code of the breaker, the split or the two sheets The reverse arrangement of the breakers is detected to judge the quality of the tire internal structure.

According to a seventh aspect of the present invention, in the tire inspection method according to the first aspect, the filtering process averages pixel densities within a predetermined region of the image data as a unit, and performs the binarization. A tire inspection method is proposed in which foreign matter is mixed into the breaker arrangement portion by comparing the image data and predetermined reference data.

According to the tire inspection method, electromagnetic wave radiation is used to dispose two breakers provided between the carcass and the tread of the tire and having a plurality of cords arranged in different directions at predetermined intervals. The image data obtained by imaging is subjected to preprocessing. In this pre-processing, after the brightness of the image data at the same image pickup position is emphasized, a plurality of image data for the same image pickup position are added, and the addition result is divided by the number of additions to average the image data. As a result, the contrast is increased and the noise component is removed. After performing this preprocessing, a predetermined density conversion process is performed on the image data, and a predetermined filter process is performed on the image data subjected to the density conversion process. In this filtering process, the pixel density is averaged within a predetermined area of the image data as a unit, and, for example, the contrast of the image is further increased and the noise component is further removed. Further, the image data obtained by the filtering process is binarized, the binarized image data is compared with predetermined reference data, and the inclusion of foreign matter into the breaker arranging portion is detected to detect the inside of the tire. The quality of the structure is determined.

Further, in the tire inspection method according to claim 1, in the filter processing, a differential processing is performed within a predetermined area of the image data as a unit, and then the image data and the density are obtained. Predetermined arithmetic processing is performed with the converted image data, density conversion processing is performed again on the post-computation image data, and the binarized image data and predetermined reference data are compared. By doing so, a tire inspection method for detecting a defect of the breaker is proposed.

According to the tire inspection method, two breakers, which are provided between the carcass and the tread of the tire and have a plurality of cords arranged in different directions at predetermined intervals, are arranged by using electromagnetic radiation. The image data obtained by imaging is subjected to preprocessing. In this pre-processing, after the brightness of the image data at the same image pickup position is emphasized, a plurality of image data for the same image pickup position are added, and the addition result is divided by the number of additions to average the image data. As a result, the contrast is increased and the noise component is removed. After performing this preprocessing, a predetermined density conversion process is performed on the image data, and a predetermined filter process is performed on the image data subjected to the density conversion process. In this filtering process, after a differentiating process is performed within a predetermined region of the image data as a unit, a predetermined arithmetic process is performed between the image data and the image data after the density conversion. The density conversion processing is performed again on the image data after the arithmetic processing, and, for example, the contrast of the image is further increased, the noise component is further removed, and the portion where the pixel density is largely changed is emphasized. Further, the image data obtained by the filter processing is binarized, the binarized image data is compared with predetermined reference data, and a defect of the breaker is detected to judge whether the tire internal structure is good or bad. To be done.

According to a ninth aspect of the present invention, in the tire inspection method according to the first aspect, the filtering process averages pixel densities in a predetermined region of the image data as a unit and the predetermined region is used as a unit. As a result, a tire inspection method is proposed, in which a differentiation process is performed in the region and the binarized image data is compared with predetermined reference data to detect a misalignment or an overlap of the ends of the breaker.

According to the tire inspection method, electromagnetic wave radiation is used to dispose two breakers provided between the carcass and the tread of the tire and having a plurality of cords arranged in different directions at predetermined intervals. The image data obtained by imaging is subjected to preprocessing. In this pre-processing, after the brightness of the image data at the same image pickup position is emphasized, a plurality of image data for the same image pickup position are added, and the addition result is divided by the number of additions to average the image data. As a result, the noise component is removed.
After performing this preprocessing, a predetermined density conversion process is performed on the image data, and a predetermined filter process is performed on the image data subjected to the density conversion process.
In this filter processing, the pixel density is averaged in a predetermined area of the image data as a unit, and the differentiation processing is performed in the predetermined area as a unit to increase, for example, the contrast of an image. At the same time, the noise component is further removed, and the portion where the pixel density changes greatly is emphasized. Further, the image data obtained by the filter processing is binarized, the binarized image data is compared with predetermined reference data, and misalignment or overlap of the ends of the breaker is detected. The quality of the tire internal structure is determined.

In the tenth aspect of the present invention, the arrangement of two breakers provided between the carcass and the tread of the tire and having a plurality of cords arranged in different directions at predetermined intervals is imaged using electromagnetic radiation. A tire inspection device for inspecting an internal structure of a tire based on image data obtained by, comprising: a radiation source for irradiating a tire to be inspected with electromagnetic radiation such as X-rays or γ-rays; Image data in which electromagnetic radiation transmitted through a predetermined imaging target position is incident and a detector for detecting energy of the electromagnetic radiation, and an energy amount of the electromagnetic radiation detected by the detector are associated with coordinates of the imaging target position. Then, after adding the image enhancing means for enhancing the brightness by enhancing the image data at the same image capturing position and the image data at the same image capturing position, An averaged image data storage unit that stores averaged image data obtained by dividing the addition result by the number of additions, a density conversion processing unit that performs a predetermined density conversion process on the averaged image data, and the density conversion process. Filter processing means for performing a predetermined filter processing on the image data subjected to the filtering, binarization means for binarizing the image data obtained by the filter processing, the binarized image data and the predetermined image data. A tire inspection apparatus is proposed, which comprises a judging means for judging whether the internal structure of the tire is good or bad by comparing with reference data, and an outputting means for outputting a judgment result by the judging means.

According to the tire inspection apparatus, the radiation source irradiates the tire to be inspected with electromagnetic radiation such as X-rays or γ-rays, and the electromagnetic radiation transmitted through the tire at a predetermined imaging target position is transmitted to the detector. Upon incidence, the energy of the electromagnetic radiation is detected. Further, the energy amount of the electromagnetic radiation detected by the detector is made into image data in correspondence with the coordinates of the imaging target position. At this time, the brightness of the image data is improved at the same imaging position to enhance the image, and
The image data obtained by performing imaging a plurality of times at the same imaging position are sequentially added at the video rate, and the addition result is stored as averaged image data obtained by dividing by the number of additions. As a result, the contrast is increased and the noise component is removed. After that, the density conversion processing unit performs a predetermined density conversion process on the averaged image data, and the image data subjected to the density conversion process is subjected to a predetermined filter process by the filter processing unit. , For example, the contrast of the image is further increased and the noise component is further removed. The image data obtained by the filtering process is binarized by the binarizing means, and the image data in which the code of the breaker and the part other than the code can be clearly distinguished are obtained. Further, the determination means compares the binarized image data with predetermined reference data to determine the quality of the internal structure of the tire, and the determination result is output by the output means.

In the eleventh aspect of the present invention, the arrangement of two breakers, which are provided between the carcass and the tread of the tire and have a plurality of cords arranged in different directions at predetermined intervals, are imaged using electromagnetic radiation. A tire inspection device for inspecting an internal structure of a tire based on image data obtained by the above, wherein the image data input means for inputting the image data and the image data input by the image data input means are imaged in the same manner. An image enhancing means for enhancing the image at the position, an averaged image data storage means for storing the averaged image data obtained by adding the image data at the same imaging position and then dividing the addition result by the number of additions, and the averaging. Density conversion processing means for performing a predetermined density conversion process on image data,
Filter processing means for performing a predetermined filter processing on the image data subjected to the density conversion processing, and binarization means for binarizing the image data obtained by the filter processing,
Tire inspection provided with a judging means for judging whether the internal structure of the tire is good or bad by comparing the binarized image data with predetermined reference data, and an outputting means for outputting a judgment result by the judging means. Suggest a device.

According to the tire inspection apparatus, two breakers, which are provided between the carcass and the tread of the tire and have a plurality of cords arranged in different directions at predetermined intervals, are arranged by using electromagnetic radiation. Image data obtained by imaging is input by the image data input means.
At this time, the brightness of the image data is improved at the same image pickup position, the image is emphasized, and the image data picked up a plurality of times at the same image pickup position are sequentially added at the video rate, The addition result is stored as averaged image data divided by the number of additions. As a result, the contrast is increased and the noise component is removed. After that, the density conversion processing unit performs a predetermined density conversion process on the averaged image data, and the image data subjected to the density conversion process is subjected to a predetermined filter process by the filter processing unit. , For example, the contrast of the image is further increased and the noise component is further removed. The image data obtained by the filtering process is binarized by the binarizing means, and the image data in which the code of the breaker and the part other than the code can be clearly distinguished are obtained. Further, the determination means compares the binarized image data with predetermined reference data to determine the quality of the internal structure of the tire, and the determination result is output by the output means.

Further, in claim 12, claim 10 or 1 is provided.
In the tire inspection apparatus according to item 1, there is proposed a tire inspection apparatus in which the filter processing unit includes an averaging processing unit that averages pixel densities in a predetermined area of the image data in the area.

According to the tire inspection apparatus, in the filter processing, the averaging processing unit averages the pixel density in a predetermined area of the image data in the area, thereby increasing the contrast. Noise components are also removed.

According to a thirteenth aspect, in the tire inspection apparatus according to the twelfth aspect, the determination means compares the breaker end position in the binarized image data with the breaker end position in the reference data. Then, a tire inspection device for detecting the disturbance of the end of the breaker is proposed.

According to the tire inspection apparatus, the determination means compares the breaker end position in the binarized image data with the reference data, that is, the breaker end position in the normal image data, Disturbances at the breaker edge are detected.

According to a fourteenth aspect, in the tire inspection apparatus according to the twelfth aspect, the determining means subtracts the reference data from the binarized image data, and based on the image data of the subtraction result. We propose a tire inspection device that detects the jumping out of a cord from the end of the breaker.

According to the tire inspection apparatus, the determination means determines the reference data from the binarized image data,
That is, the normal image data is subtracted. This makes it possible to obtain image data in which the pixels of the code protruding from the breaker end are clear. Based on this image data,
Jumping out of the cord from the end of the breaker is detected.

According to a fifteenth aspect, in the tire inspection apparatus according to the twelfth aspect, the determination processing compares the binarized image data with the reference data to determine whether the breaker is arranged. We propose a tire inspection device that detects the mixture of foreign matter.

According to the tire inspection apparatus, the judging means compares the binarized image data with the reference data, that is, the image data in the normal state. As a result, the pixels of the foreign matter mixed in the breaker arrangement portion become clear. Based on this image data, the mixing of foreign matter into the breaker arrangement portion is detected.

Further, in claim 16, claim 10 or 1
1. In the tire inspection apparatus according to 1, the filter processing unit averages pixel densities within a predetermined region of the image data as a unit, and an average processing unit differentiates within a predetermined region as a unit. There is proposed a tire inspection device including a differential processing means for performing processing.

According to the tire inspection apparatus, in the filtering process, the averaging processing unit averages the pixel density in a predetermined region of the image data within the region, and the differential processing unit Differentiation is performed in the predetermined region as a unit, and, for example, the contrast of the image is further increased, the noise component is further removed, and the portion where the pixel density greatly changes is emphasized.

According to a seventeenth aspect, in the tire inspection apparatus according to the sixteenth aspect, the judging means compares each pixel density of the binarized image data with each pixel density of the reference data. Thus, a tire detection device for detecting the opening of the breaker seam is proposed.

According to the tire inspection apparatus, the determination means compares the pixel density of the binarized image data with the reference data, that is, the pixel density of the image data in the normal state, and determines the breaker. Opening of the seam is detected.

In the eighteenth aspect, in the tire inspection apparatus according to the sixteenth aspect, the judging means compares each pixel density of the binarized image data with each pixel density of the reference data. Accordingly, a tire detection device that detects the overlap of the breaker seams is proposed.

According to the tire inspection apparatus, the determination means compares the pixel density of the binarized image data with the reference data, that is, the pixel density of the image data in the normal state, and determines the breaker. Overlap of seams is detected.

Further, in claim 19, claim 10 or 1
The tire inspection apparatus according to item 1, wherein the filter processing unit includes a differential processing unit that performs a differential process within a predetermined area of the image data as a unit.

According to the tire inspection apparatus, in the filter processing, the differentiation processing unit performs the differentiation processing in the predetermined area in units of the predetermined area to further increase the contrast of the image and further increase the noise component. The portions that have been removed and the pixel density has changed significantly are emphasized.

According to a twentieth aspect of the invention, in the tire inspection apparatus according to the nineteenth aspect of the invention, the judging means compares the binarized image data with the reference data and breaks the end cord of the breaker. We propose a tire inspection device that detects dislocations and dislocations.

According to the tire inspection apparatus, the binarized image data is compared with the reference data, that is, the image data in the normal state by the judging means, and the judgment is made based on the detected position of each code of the breaker. Breakers, breaks and side-by-side breaker end cords are detected.

Further, in claim 21, claim 10 or 1.
In the tire inspection apparatus according to item 1, the filter processing means performs a differential processing within a predetermined area of the image data as a unit, and the differential processed image data and the density conversion processing means. And a second density conversion processing means for performing density conversion processing again on the image data after the arithmetic processing. We propose a tire inspection system.

According to the tire inspection apparatus, in the filter processing, the differential processing unit performs the differential processing within the predetermined area of the image data as a unit, and the arithmetic processing unit performs the differential processing. After a predetermined arithmetic processing is performed between the image data subjected to the density conversion and the density-converted image data by the density conversion processing means, the second
The density conversion processing means again performs the density conversion processing on the image data after the arithmetic processing, and further increases the contrast of the image and further removes the noise component, and the pixel density is largely changed. The part is emphasized.

According to a twenty-second aspect, in the tire inspection apparatus according to the twenty-first aspect, the determining means compares the binarized image data with the reference data to detect a breaker defect. Propose an inspection device.

According to the tire inspection apparatus, the determination means compares the binarized image data with the reference data, and detects a defect such as a cord or a rubber folded in the breaker.

According to a twenty-third aspect, in the tire detection apparatus according to the twenty-first aspect, the determining means compares the binarized image data with the reference data, and the end portion of the breaker is misaligned. Alternatively, we propose a tire inspection device that detects line-up and overlap.

According to the tire inspection apparatus, the judging means compares the binarized image data with the reference data and detects misalignment or overlap of the ends of the breaker.

According to a twenty-fourth aspect of the present invention, the arrangement of two breakers provided between the carcass and the tread of the tire and having a plurality of cords arranged in different directions at a predetermined interval is imaged by using electromagnetic radiation. A tire inspection device for inspecting an internal structure of a tire based on image data obtained by the above, wherein the image data input means for inputting the image data and the image data input by the image data input means are imaged in the same manner. Image enhancement means for enhancing the image data at the position, averaged image data storage means for storing the averaged image data obtained by adding the image data at the same imaging position and dividing the addition result by the number of additions, Density conversion processing means for performing a predetermined density conversion process on the averaged image data, and for the image data subjected to the density conversion process, An averaging process for averaging pixel densities within the region in a fixed region, a differentiation process for performing a differentiation process in the region for a predetermined region of the image data, the image data subjected to the differentiation process, and Each of the arithmetic processing for performing a predetermined arithmetic operation on the image data after the density conversion by the density conversion processing means and the second density conversion processing for performing the density conversion processing again on the image data after the arithmetic processing is time-shared. Filter processing means, the binarizing means for binarizing the image data obtained by the filtering, the breaker end position in the binarized image data, and the breaker end position in the reference data. And comparing the reference data from the binarized image data with the first determination means for detecting the disturbance at the end of the breaker, and the subtraction result Second judging means for detecting the jumping out of the code from the end of the breaker based on the image data is compared with each pixel density of the binarized image data and each pixel density of the reference data. By comparing the pixel density of the binarized image data with the pixel density of the reference data, the third determination means for detecting the seam of the breaker is compared with the third determination means. A fourth determination means for detecting an overlap of a seam and a fifth determination means for comparing the binarized image data with the reference data to detect a break, a dislocation, and an inversion of the end code of the breaker. The judging means compares the binarized image data with the reference data to detect sixth foreign matter from the breaker arrangement part, and the binarizing means. Seventh determination means for comparing the image data with the reference data to detect a defect of the breaker is compared with the binarized image data and the reference data, and the end portion of the breaker is misaligned. Alternatively, there is proposed a tire inspection device that includes an eighth determination unit that detects a line overlap and an output unit that outputs a determination result of the determination unit.

According to the tire inspection apparatus, the radiation source irradiates the tire to be inspected with electromagnetic radiation such as X-rays or γ-rays, and the electromagnetic radiation transmitted through the tire at a predetermined imaging target position is transmitted to the detector. Upon incidence, the energy of the electromagnetic radiation is detected.

Further, the energy amount of the electromagnetic radiation detected by the detector is made into image data in correspondence with the coordinates of the image pickup target position. At this time, the brightness of the image data is improved at the same image pickup position to enhance the image, and the image data picked up a plurality of times at the same image pickup position are sequentially added at the video rate, and then the addition is performed. The result is stored as averaged image data divided by the number of additions. As a result, the contrast is increased and the noise component is removed.

Thereafter, the density conversion processing means performs predetermined density conversion processing on the averaged image data,
The image data that has been subjected to the density conversion processing is subjected to predetermined filter processing by the filter processing means.

The filtering process includes an averaging process,
Differentiating processing, arithmetic processing, and density converting processing are provided, and these processings are executed in a time-sharing manner by a predetermined command.

Further, in the averaging process, the image density subjected to the density conversion process is averaged in a predetermined region of the image data in the region, and in the differential process, the image data is averaged. In the arithmetic processing, a predetermined calculation is performed between the image data subjected to the differential processing and the density-converted image data by the density conversion processing means. In the density conversion processing, the density conversion processing is performed again on the image data after the arithmetic processing. As a result, for example, the contrast of the image is further increased, the noise component is further removed, and the portion where the pixel density greatly changes is emphasized.

The image data obtained by the filter processing is binarized by the binarizing means, and the image data in which the breaker code and the part other than the code can be clearly distinguished are obtained.

Further, the first to the eighth determining means compare the binarized image data with predetermined reference data to determine the quality of the internal structure of the tire,
The determination result is output by the output means.

At this time, the first judging means compares the breaker end position in the binarized image data with the breaker end position in the reference data to detect the disturbance of the breaker end. In the second determination means, the reference data is subtracted from the binarized image data, and the jumping out of the code from the end of the breaker is detected based on the subtracted image data. The means compares the pixel densities of the binarized image data with the pixel densities of the reference data to detect the opening of the breaker seam, and the fourth determination means performs the binarization. The pixel densities of the image data and the pixel densities of the reference data are compared to detect the overlap of the breaker seams.

Further, in the fifth judging means, the binarized image data and the reference data are compared with each other, and breakage, dislocation and reverse arrangement of the end code of the breaker are detected. In the means, the binarized image data and the reference data are compared with each other to detect the inclusion of foreign matter in the breaker arrangement portion, and in the seventh determining means, the binarized image data and the reference data. Data is compared to detect a breaker defect, and the eighth determination means compares the binarized image data with the reference data to detect misalignment or overlap of end portions of the breaker. To be done.

A twenty-fifth aspect of the present invention proposes the tire inspection apparatus according to the twenty-fourth aspect, wherein the filter processing means comprises one piece of hardware and performs the time-divisional filter processing.

According to the tire inspection apparatus, the filter processing means is composed of one piece of hardware and performs the time-divisional filter processing.

[0081]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will be described below with reference to the drawings. FIG. 1 is a block diagram showing a tire inspection device according to a first embodiment of the present invention. In the figure, 10 is a tire inspection apparatus main body (hereinafter referred to as an apparatus main body), 21 is an X-ray source, and 22 is an X-ray camera.

The apparatus main body 10 includes an image measuring unit 11, a preprocessing unit 12, a display switching unit 13, a display unit 14, a storage unit 15, a data input unit 16, a keyboard 17, and a video printer 1.
8 and a data display monitor 19.

The image measuring section 11 includes a plurality of measuring units 1.
1a to 11h and a main CPU 11i, and each measurement unit 11a to 11h inputs image data from the X-ray camera 22 via the preprocessing unit 12 and reference data from the data input unit 16, and each measurement unit 11a.
The inspection determinations assigned to 11h are output, the result is output to the host CPU 11i, and the image in the inspection processing process is output to the main CPU 11i and the display switching unit 13.

The main CPU 11i is for each measuring unit 1
The determination result is input from 1a to 11h, and the overall determination is performed based on this.

The pre-processing unit 12 comprises an emphasis processing unit 12a, an average addition processing unit 12b, and image memories 12c and 12d. Image data input from the X-ray camera 22 will be described later by the emphasis processing unit 12a and the average addition processing unit 12b. The image processing is performed and output to the image measuring unit 11.

The display switching unit 13 switches the output images of the measuring units 11a to 11h based on the control signal from the main CPU 11i and outputs the images to the display unit 14.

The storage section 15 stores the output image data of the measuring units 11a to 11h or the data in the process of processing under the control of the main CPU 11i.

The data input section 16 is composed of, for example, a floppy disk drive device or the like, and is used for inputting data as a reference in the determination of inspection.

The keyboard 17 is used to input various data and commands to the main CPU 11i and the measuring units 11a to 11h.

The X-ray source 21 irradiates the tire to be inspected with X-rays, the X-rays are input to the X-ray camera 22, and the image of the portion to be inspected of the tire is captured by the X-ray camera 22.

In this embodiment, each measuring unit 11a-1
Different inspection target items are assigned to 1h. That is, the measurement unit 11a determines whether or not the end cord 4a of the breaker 4 shown in FIG. 11 is disturbed, and the measurement unit 11b determines whether or not the end cord 4a of the breaker 4 shown in FIG. 13 is popped out. It is determined whether or not. Further, the measuring unit 11c determines whether or not the seam of the breaker 4 shown in FIG. 8 is opened, and the measuring unit 11d determines whether or not the seam of the breaker 4 shown in FIG. 7 is overlapped. There is. Measuring unit 11e
Determines whether or not the end cords of the breaker 4 shown in FIGS. 6, 10, and 12 are broken, dislocated, and reversed.
The measuring unit 11f determines whether or not a foreign matter is mixed in the breaker arrangement portion shown in FIG. further,
The measuring unit 11g determines whether or not the breaker 4 shown in FIG. 14 is defective, and the measuring unit 11h is shown in FIG.
It is determined whether there is a misalignment or an overlap of the ends of the breaker 4 shown in FIG.

Next, the operation of the first embodiment having the above configuration will be described. In the first embodiment, an image is picked up by an X-ray eight times at one inspection target portion, and the image data is processed by the preprocessing unit 12. Then, the preprocessed image data is input to the image measuring unit 11.

In the preprocessor 12, as shown in the flowchart of FIG. 16, the image signal is input as an analog signal from the X-ray camera 22 (SA1), and the analog signal is enhanced (SA2). This emphasis processing is performed to improve the image density and remove noise during transmission.
Thereafter, the emphasized image signal is digitally converted (SA3) and stored in the image memory 12c (SA).
4).

After this, the average addition processing is performed (SA
5) When it is stored in the image memory 12d (SA6), both are compared with the number of times of processing (SA7), and as the processing result, the image data obtained by this processing is output to the image measuring unit 11 (SA8).

Here, the average addition processing means that the input image data and the stored image data are sequentially added at the video rate a number of times, and then the addition result is divided by the number of additions.
The process of obtaining one image data. By this average addition processing, the noise component included in the image data can be reduced.

The image data input from the preprocessing unit 12 to the image measuring unit 11 is input to each of the eight measuring units 11a to 11h. Each measuring unit 11a-11h
When the image data is input to each measurement unit 11a-
In 11h, the processing assigned to each is performed as described above.

That is, in the measuring unit 11a, as shown in the flowchart of FIG. 17, after the image data is input from the preprocessing unit 12 (SB1), the density conversion process and the averaging process are performed (SB2, SB3). The density conversion process performed here is a brightness inversion process. Further, in the averaging process, an area having a predetermined number of vertical and horizontal pixels in the image data is set as a unit, and the pixel densities are averaged for each unit area. As a result, the noise component is reduced and the extracted portion has a uniform density.

Next, binarization processing of the image data is performed (SB4). In this binarization process, each pixel density is divided by a predetermined threshold value and expressed as a light and dark binary value. Next, correction processing such as expansion, hole filling, and thinning is performed (SB5), and the image data obtained by this is compared with the reference data that has been input in advance, and the end code 4a of the breaker 4 is disturbed. Whether or not it is determined (SB6).

At the time of this determination, since the edge of the breaker 4 is curving and wavy in the image, it is determined whether or not the boundary line where the density level of the image changes is linear.

Next, the result of this determination is sent to the main CPU 11
It is output to i (SB7), and the process ends.

In the measuring unit 11b, as shown in the flowchart of FIG. 18, after the image data is input from the preprocessing unit 12 (SC1), the density conversion process and the averaging process are performed (SC2, SC3). The density conversion process performed here is
This is the brightness inversion process. Further, in the averaging process, an area having a predetermined number of vertical and horizontal pixels in the image data is set as a unit, and the pixel densities are averaged for each unit area. As a result, the noise component is reduced and the extracted portion has a uniform density.

Next, binarization processing of the image data is performed (SC4). In this binarization process, each pixel density is divided by a predetermined threshold value and expressed as a light and dark binary value. Next, correction processing such as expansion, filling, and contraction is performed (SC5), and the image data obtained by this is compared with reference data that has been input in advance, and whether the end code 4a of the breaker 4 is popped out. Whether or not it is determined is performed (SC6).

In this determination, since several wire cords 4a are protruding from the end of the breaker 4, the density level of the portion of the wire cord 4a is lower than that of the periphery, so that this portion is extracted. The protrusion of the wire cord 4a is detected.

Then, the result of this determination is sent to the main CPU 11
It is output to i (SC7), and the process ends.

In the measuring unit 11c, as shown in the flowchart of FIG. 19, after the image data is input from the preprocessing unit 12 (SD1), the density conversion process and the averaging process are performed (SD2, SD3). In the density conversion process performed here, the brightness inversion process is performed, and the density conversion is performed using conversion formulas such as linear conversion, quadratic curve conversion, cubic curve conversion, gamma curve conversion, and log curve conversion. Further, in the averaging process, an area having a predetermined number of vertical and horizontal pixels in the image data is set as a unit, and the pixel densities are averaged for each unit area. As a result, the noise component is reduced and the extracted portion has a uniform density.

Then, m × n processing is performed (SD4). This m × n process is a mask process for performing a differentiating process of pixel density in a region of a predetermined size, and m and n are matrix sizes, and coefficients are provided corresponding to each pixel in this region. , Are weighted.

After this process, the image data is binarized (SD5). In this binarization process, each pixel density is divided by a predetermined threshold value and expressed as a light and dark binary value. Then delete,
Correction processing such as expansion and contraction is performed (SD6), and the image data obtained by this is compared with reference data input in advance to determine whether or not there is an open seam of the breaker 4 ( SD7).

Since the material of the breaker 4 is strip-shaped, when it is wound around the carcass 3, a seam is formed at the start point and the end point. When the gap between the start point and the end point is open, there is a portion where the wire cord 4a does not exist, so that this portion has a higher concentration level than the surrounding portion where the wire cord 4a exists. Thereby, the opening of the seam of the breaker 4 is detected. Further, it is detected which of the two breakers 4 has the seam opening depending on the direction of the wire cord.

Then, the result of this determination is sent to the main CPU 11
It is output to i (SD8), and the process is terminated.

In the measuring unit 11d, as shown in the flowchart of FIG. 20, after the image data is input from the preprocessing section 12 (SE1), the density conversion processing and the m × n processing similar to the above are performed (SE2, SE3). ). In the density conversion process performed here, the brightness inversion process is performed, and the linear conversion,
Quadratic curve conversion, cubic curve conversion, gamma curve conversion, log
The density conversion is performed using a conversion formula such as curve conversion. Further, in the m × n process, a mask process is performed in which a pixel density differential process or the like is performed within a region of a predetermined size. m and n are sizes of the matrix, and a coefficient is provided and weighted corresponding to each pixel in this area. As a result, the portion where the pixel density is changing is emphasized.

After this processing, binarization processing of image data is performed (SE4). In this binarization process, each pixel density is divided by a predetermined threshold value and expressed as a light and dark binary value. Then delete,
Performs correction processing such as expansion, thinning, and deletion (SE
5) Then, the image data thus obtained is compared with the reference data input in advance, and a process of determining whether or not the seam of the breaker 4 overlaps is performed (SE6).

Since the material of the breaker 4 is strip-shaped, when it is wound around the carcass 3, a seam is formed at the start point and the end point. When the start point and the end point are overlapped with each other, the density level is lower in this portion because there are more wire cords 4a than in the periphery. This allows breaker 4
The overlap of the seams is detected. Further, it is detected which of the two breakers 4 the seam overlaps, depending on the direction of the wire cord.

Next, the result of this determination is sent to the main CPU 11
It is output to i (SE7), and the process ends.

In the measuring unit 11e, as shown in the flowchart of FIG. 21, after the image data is input from the preprocessing unit 12 (SF1), the density conversion process and the m × n process similar to the above are performed (SF2, SF3). ). In the density conversion process performed here, a brightness inversion process is performed.

Further, in the m × n process, a mask process is performed in which a pixel density differential process or the like is performed within an area of a predetermined size. m and n are sizes of the matrix, and a coefficient is provided and weighted corresponding to each pixel in this area. As a result, the portion where the pixel density is changing is emphasized.

After this processing, binarization processing of image data is performed (SF4). In this binarization process, each pixel density is divided by a predetermined threshold value and expressed as a light and dark binary value. Next, a correction process such as deleting a noise component is performed (SF5), and the image data obtained by this is compared with the reference data input in advance, and the end code 4a of the breaker 4 is compared.
Then, it is determined whether or not there is a fold, a dislocation, and a rearrangement (SF6).

Here, the wire cord 4a is not detected at the normal position unless the wire cord 4a is present at the normal position due to the wire cord 4a being broken or broken. Further, if the two breakers 4 are arranged in an opposite direction, the wire cords 4a will be in a different direction from the normal direction, and thus the arrangement reverse can be detected.

Next, the result of this determination is sent to the main CPU 11
It is output to i (SF7), and the process ends.

In the measuring unit 11f, as shown in the flowchart of FIG. 22, after the image data is input from the preprocessing unit 12 (SG1), the density conversion process and the averaging process are performed (SG2, SG3). In the density conversion processing performed here, density conversion is performed using conversion expressions such as linear conversion, quadratic curve conversion, cubic curve conversion, gamma curve conversion, and log curve conversion. Further, in the averaging process, an area having a predetermined number of vertical and horizontal pixels in the image data is set as a unit, and the pixel densities are averaged for each unit area. As a result, the noise component is reduced and the extracted portion has a uniform density.

Next, binarization processing of the image data is performed (SG4). In this binarization process, each pixel density is divided by a predetermined threshold value and expressed as a light and dark binary value. Next, correction processing is performed in the order of deletion, expansion, thinning, and deletion (SG5),
The image data obtained in this way is compared with the reference data input in advance, and a determination process is performed as to whether or not the foreign matter 7 is mixed in the arrangement portion of the breaker 4 (SG6).

Here, if the foreign matter 7 is mixed in a part of the breaker 4, the density level of the foreign matter 7 part becomes lower than that of the surroundings, and this can be detected.

Then, the result of this determination is sent to the main CPU 11
It is output to i (SG7), and the process ends.

In the measuring unit 11g, as shown in the flowchart of FIG. 23, after the image data is input from the preprocessing unit 12 (SH1), the density conversion process and the m × n process similar to the above are performed (SH2, SH3). ). In the density conversion processing performed here, density conversion is performed using conversion expressions such as linear conversion, quadratic curve conversion, cubic curve conversion, gamma curve conversion, and log curve conversion.

Further, in the m × n process, a mask process is performed in which a pixel density differential process or the like is performed within a region of a predetermined size. m and n are sizes of the matrix, and a coefficient is provided and weighted corresponding to each pixel in this area. As a result, the portion where the pixel density is changing is emphasized.

After this processing, arithmetic processing between image data is performed (SH4). In this processing, the SH3 processing is performed based on the image data subjected to the density conversion in the SH2 processing.
The image data subjected to the m × n processing is subtracted.

Then, after the density conversion processing is performed again (S
H5), the binarization processing of the image data is performed (SH6). In this binarization process, each pixel density is divided by a predetermined threshold value and expressed as a light and dark binary value. Then delete, dilate, deflate, delete,
Correction processing is performed in the order of expansion and thinning (SH
7) Then, the image data thus obtained is compared with the reference data input in advance, and a determination process as to whether or not the breaker 4 is defective is performed (SH8).

Here, when the breaker 4 is defective, the wire cord 4a and rubber are folded,
Since the density level is lower than that of the grid portion of the normal wire cord 4a, this can be detected.

Then, the result of this determination is sent to the main CPU 11
It is output to i (SH9), and the process ends.

In the measuring unit 11h, as shown in the flowchart of FIG. 24, after the image data is input from the preprocessing unit 12 (SI1), the density conversion process and the averaging process are performed (SI2, SI3). The density conversion process performed here is a 1: 1 conversion process. Further, in the averaging process, an area having a predetermined number of vertical and horizontal pixels in the image data is set as a unit, and the pixel densities are averaged for each unit area. As a result, the noise component is reduced and the extracted portion has a uniform density.

Then, m × n processing is performed (SI4). This m × n process is a mask process for performing a differentiating process of pixel density in a region of a predetermined size, and m and n are matrix sizes, and coefficients are provided corresponding to each pixel in this region. , Are weighted.

After this processing, binarization processing of image data is performed (SI5). In this binarization process, each pixel density is divided by a predetermined threshold value and expressed as a light and dark binary value. Then delete,
Correction processing such as expansion and thinning is performed (SI6), and the image data obtained by this is compared with reference data that has been input in advance to determine whether or not there is a misalignment or an overlap of the ends of the breaker 4. Is performed (SI
7).

In this case, the concentration level changes rapidly along the edge of the breaker 4, or the breaker 4
These states are detected because there is no change in density level in two steps in the width direction.

Then, the result of this determination is sent to the main CPU 11
It is output to i (SI8), and the process ends.

As described above, each measuring unit 11a-1
In 1h, each processing and determination is performed, and each determination result is input to the main CPU 11i.
The PU 11i makes a comprehensive judgment based on these judgment results, and displays the judgment result on the data display monitor 19.

Further, the image data in the processing process of each of the measuring units 11a to 11h is stored in the storage unit 15 via the main CPU 11i, and these images are displayed on the display unit 14 by operating the display switching unit 13. It can be displayed on the display.

Further, from the keyboard 17 to the main CPU
By inputting a command to 11i, a desired image can be printed out from the video printer 18 as needed.

As a result, according to the present embodiment, it is possible to greatly reduce the labor of the inspector when the placement of the breaker 4 and the state of the wire cord 4a are inspected, and the inspection time is much longer than in the conventional case. And the inspection accuracy can be improved.

Next, a second embodiment of the present invention will be described. FIG. 25 is a block diagram showing a tire inspection device according to the second embodiment. In the figure, the same components as those in the first embodiment described above are denoted by the same reference numerals, and description thereof will be omitted. Further, the difference between the first embodiment and the second embodiment is that the image measurement unit 11 is provided with a filter unit 11j configured by hardware, and density conversion processing, averaging processing, m × n processing, and calculation are performed. The filter processing such as processing is time-divisionally performed by the filter unit 11j, and the result is distributed to the respective measurement units 11a to 11h.

As a result, the filtering process can be performed at high speed, and the overall inspection time can be shortened significantly.

Next, the operation of the second embodiment having the above configuration will be described. In the second embodiment, an image is picked up by X-rays eight times in one examination target region, and this image data is processed by the preprocessing unit 12 as in the first embodiment. Then, the preprocessed image data is input to the hardware unit 11j of the image measuring unit 11.

As in the first embodiment, the preprocessing unit 12 inputs an image signal as an analog signal from the X-ray camera 22 as shown in the flowchart of FIG. 16 (SA
1), emphasis processing is performed on this analog signal (SA
2). This emphasis processing is performed to improve the image density and remove noise during transmission. Thereafter, the emphasized image signal is digitally converted (SA3) and stored in the image memory 12c (SA4).

After this, the average addition processing is performed (SA
5) When it is stored in the image memory 12d (SA6), both are compared with the number of times of processing (SA7), and as the processing result, the image data obtained by this processing is output to the hard unit 11j of the image measuring unit 11 (SA8).

Here, the average addition processing means that the input image data and the stored image data are sequentially added at the video rate only a number of times, and then the addition result is divided by the number of additions.
The process of obtaining one image data. By this average addition processing, the noise component included in the image data can be reduced.

The image data input from the pre-processing section 12 to the hardware unit 11j corresponds to each measurement unit 11j.
After being time-divisionally filtered into a state necessary for the processing of a to 11h, it is distributed to each of the eight measuring units 11a to 11h. At this time, part of the measurement unit supports the filtering process.

When the image data is input from the hardware unit 11j to each of the measuring units 11a to 11h, the processing assigned to each of the measuring units 11a to 11h is performed in the same manner as described above.

That is, in the hard unit 11j, FIG.
28 through 28, the preprocessing unit 1
It is monitored whether image data is input from 2 (SJ
1) When the image data is input, the image data (hereinafter, referred to as an original image) and the processing start command are first and seventh.
To the measuring units 11a and 11g (SJ2).
As a result, the first measurement unit 11a performs a density conversion process that inverts the luminance of the original image, and the seventh measurement unit 11g performs a linear conversion, a quadratic curve conversion, a cubic curve conversion, and a gamma conversion on the original image. Density conversion is performed using conversion formulas such as curve conversion and log curve conversion.

Next, the hardware unit 11j performs averaging processing and m × n processing on the original image (SJ3, SJ).
4). In this averaging process, an area consisting of a predetermined number of vertical and horizontal pixels in the image data is used as a unit, and the pixel density of each unit area is averaged. As a result, the noise component is reduced and the extracted portion has a uniform density. Further, the m × n process is a mask process for performing a differentiating process of pixel density in a region of a predetermined size, and m and n are matrix sizes, and a coefficient is provided for each pixel in this region. Are weighted.

Thereafter, the image data obtained by performing the averaging process and the m × n process is output to the eighth measuring unit 11h as a processing command (SJ5). As a result, the eighth measuring unit 11h determines whether or not the ends of the breaker 4 are misaligned and overlapped.

Next, the hardware unit 11j takes in the image data obtained by subjecting the original image to the density conversion processing in the first measuring unit 11a from the first measuring unit 11a (SJ6), and acquiring this image data as the first image data. 3 to the measuring unit 11c (SJ7). As a result, the third measurement unit 11c performs the density conversion process that is a part of the filter process.

Then, from the seventh measuring unit 11g,
The image data in which the density conversion processing has been performed on the original image in the seventh measurement unit 11g is taken in (SJ8), and the averaging processing and density conversion (inversion) are performed on this image data.
After performing the processing (SJ9, SJ10), the image data and the processing instruction obtained thereby are processed by the sixth measurement unit 1
It is output to 1f (SJ11).

The averaging process performed here is as described above.
The area of a predetermined number of pixels in the vertical and horizontal directions in the image data is set as a unit, and the pixel densities are averaged within the area for each unit area. This reduces the noise component and makes the extracted portion uniform. Made to concentration. The density conversion (reversal) process is a brightness reversal process.

As a result, in the sixth measuring unit 11f, the breaker determines whether or not foreign matter is partially mixed.

Next, the hardware unit 11j takes in from the seventh measuring unit 11g the image data in which the density conversion processing has been performed on the original image in the seventh measuring unit 11g (SJ12), and with respect to this image data Density conversion (reversal) processing, m × n processing, subtraction processing, and density conversion processing in this order (SJ13, SJ14, SJ1).
5, SJ16), and the image data and the processing command obtained thereby are output to the seventh measuring unit 11g (SJ).
17).

The first density conversion process performed here is the brightness inversion process, and the m × n process is the same process as described above. Further, in the subtraction process, the image data subjected to the m × n process of SJ14 is subtracted from the image data subjected to the density conversion in the process of SJ13. Further, in the second density conversion processing, the density of each pixel in the image data is n times, preferably 5 times.

The seventh measuring unit 11g, to which this image data is input, uses the breaker 4 based on this image data.
It is determined whether or not there is a defect.

After that, the hardware unit 11j takes in the image data obtained by subjecting the original image to the density conversion processing in the first measuring unit 11a from the first measuring unit 11a (SJ18), and with respect to this image data. Mx
After performing the n processing (SJ19), the image data and the processing command obtained thereby are output to the fifth measurement unit 11e (SJ20).

The fifth measuring unit 11e, to which this image data is input, uses the breaker 4 based on this image data.
It is determined whether or not the end cord 4a of B is broken, dislocated, or reversed.

Next, the hardware unit 11j takes in the image data obtained by subjecting the original image to the density conversion processing in the first measuring unit 11a from the first measuring unit 11a (SJ21), and with respect to this image data. After performing the averaging process (SJ22), the image data and the processing command obtained thereby are output to the first and second measurement units 11a and 11b (SJ23). This allows
The first measuring unit 11a determines whether or not the end code of the breaker 4 is disturbed, and the second measuring unit 11b determines whether or not the end code of the breaker 4 is popped out. Be seen.

After that, the hardware unit 11j takes in the image data obtained by subjecting the original image to the density conversion processing in the third measuring unit 11c from the third measuring unit 11c (SJ24), and with respect to this image data. Mx
After performing the n processing (SJ25), the image data and the processing command obtained thereby are output to the fourth measurement unit 11d (SJ26). As a result, the fourth measurement unit 11d determines whether or not the seam of the breaker 4 overlaps.

Next, the hardware unit 11j takes in from the third measuring unit 11c the image data in which the density conversion processing has been performed on the original image in the third measuring unit 11c (SJ27), and with respect to this image data After performing averaging processing and m × n processing in this order (SJ28, S
J29), and outputs the image data and the processing command thus obtained to the third measuring unit 11c (SJ30),
The process for one captured image ends. This allows
In the third measuring unit 11c, it is determined whether or not there is an open seam of the breaker 4.

On the other hand, the first measuring unit 11a is shown in FIG.
As shown in the flowchart of FIG.
It is monitored whether or not image data and a processing command are input from j (SK1). When this is input, density conversion processing is performed on the input image data (SK2). This density conversion process is a brightness inversion process.

Next, the measuring unit 11a monitors whether or not an image data read command is input from the hardware unit 11j (SK3). When the read command is input, the image subjected to the density conversion process in the process of SK2 is monitored. The data is output to the hardware unit 11j (SK4), and the process proceeds to the process of SK3.

When no read command is input,
It is determined whether the image data and the processing command are input from the hard unit 11j (SK5). If they are not input, the process proceeds to the process of SK3. Conversion processing is performed (SK6).
In this binarization process, each pixel density is divided by a predetermined threshold value and expressed as a light and dark binary value. Next, correction processing such as expansion, filling in holes, and thinning is performed (SK7), and the image data obtained by this is compared with reference data input in advance to find that the end code 4a of the breaker 4 is disturbed. Whether or not it is determined is performed (SK8).

At the time of this determination, since the edge of the breaker 4 is curving and wavy in the image, it is determined whether or not the boundary line where the density level of the image changes is linear.

Then, the result of this determination is sent to the main CPU 11
It is output to i (SK9), and the process for one captured image ends.

The second measuring unit 11b is shown in FIG.
As shown in the flowchart of FIG.
It is monitored whether or not the image data and the processing command are input from j (SL1), and when they are input, the input image data is binarized (SL2). In this binarization process, each pixel density is divided by a predetermined threshold value and expressed as a light and dark binary value. Next, correction processing such as expansion, filling, and contraction is performed (SL3), and the image data obtained by this is compared with the previously input reference data to determine whether the end code 4a of the breaker 4 is popped out. Whether or not it is determined is performed (SL4).

In this determination, since several wire cords 4a are protruding from the end of the breaker 4, the density level of the portion of the wire cord 4a is lower than that of the periphery, so that this portion is extracted. The protrusion of the wire cord 4a is detected.

Then, the result of this determination is sent to the main CPU 11
It is output to i (SL5), and the process for one captured image ends.

The third measuring unit 11c is shown in FIG.
As shown in the flowchart of FIG.
It is monitored whether image data and a processing command are input from j (SM1). When this is input, density conversion processing is performed on the input image data (SM2). In this density conversion process, the brightness of each pixel in the image data is multiplied by n, preferably 5 times.

After that, the measuring unit 11c monitors whether or not an image data read command is input from the hardware unit 11j (SM3), and when the read command is input, the density conversion process is performed in the process of SM2. The image data is output to the hardware unit 11j (SM4), and the process of SM3 is performed.

When no read command is input,
It is determined whether or not the image data and the processing command are input from the hardware unit 11j (SM5). If these are not input, the process proceeds to the processing of SM3. Conversion processing is performed (SM6).
In this binarization process, each pixel density is divided by a predetermined threshold value and expressed as a light and dark binary value. Next, correction processing such as deletion, expansion, and contraction is performed (SM7), and the image data obtained by this is compared with the reference data input in advance to determine whether or not there is an open seam of the breaker 4. A determination process is performed (SM8).

Since the material of the breaker 4 is strip-shaped, when it is wound around the carcass 3, a seam is formed at the start point and the end point. When the gap between the start point and the end point is open, there is a portion where the wire cord 4a does not exist, so that this portion has a higher concentration level than the surrounding portion where the wire cord 4a exists. Thereby, the opening of the seam of the breaker 4 is detected. Further, it is detected which of the two breakers 4 has the seam opening depending on the direction of the wire cord.

Then, the result of this determination is sent to the main CPU 11
It is output to i (SM9), and the process for one captured image ends.

The fourth measuring unit 11d is shown in FIG.
As shown in the flowchart of FIG.
It is monitored whether or not the image data and the processing command are input from j (SN1), and when these are input, the input image data is binarized (SN2). In this binarization process, each pixel density is divided by a predetermined threshold value and expressed as a light and dark binary value. Next, correction processing such as deletion, expansion, thinning, and deletion is performed (SN3), and the image data obtained by this is compared with the reference data input in advance,
It is determined whether or not the breaker 4 has a seam overlap (SN4).

Since the material of the breaker 4 is strip-shaped, when it is wound around the carcass 3, a seam is formed at the start point and the end point. When the start point and the end point are overlapped with each other, the density level is lower in this portion because there are more wire cords 4a than in the periphery. This allows breaker 4
The overlap of the seams is detected. Further, it is detected which of the two breakers 4 the seam overlaps, depending on the direction of the wire cord.

Then, the result of this determination is sent to the main CPU 11
It is output to i (SN5), and the process for one captured image ends.

Further, the fifth measuring unit 11e is shown in FIG.
As shown in the flowchart of FIG.
It is monitored whether or not the image data and the processing command are input from j (SO1), and when they are input, the input image data is binarized (SO2). In this binarization process, each pixel density is divided by a predetermined threshold value and expressed as a light and dark binary value. Next, a correction process such as deleting a noise component is performed (SO3), and the image data obtained by this is compared with the reference data that is input in advance, and the end code 4a of the breaker 4 is bent or broken. And a process for determining whether or not there is an inversion (SO4).

Here, the wire cord 4a is not detected at the normal position unless the wire cord 4a is present at the normal position due to the wire cord 4a being broken or broken. Further, if the two breakers 4 are arranged in an opposite direction, the wire cords 4a will be in a different direction from the normal direction, and thus the arrangement reverse can be detected.

Then, the result of this determination is sent to the main CPU 11
It is output to i (SO5), and the process for one captured image ends.

Further, the sixth measuring unit 11f is shown in FIG.
As shown in the flowchart of FIG.
It is monitored whether or not the image data and the processing command are input from j (SP1). When these are input, the input image data is binarized (SP2). In this binarization process, each pixel density is divided by a predetermined threshold value and expressed as a light and dark binary value. Next, correction processing is performed in the order of deletion, expansion, thinning, and deletion (SP3), and the image data obtained by this is compared with the previously input reference data,
A determination process is performed as to whether or not the foreign matter 7 is mixed in the arrangement portion of the breaker 4 (SP4).

Here, when the foreign matter 7 is mixed in a part of the breaker 4, the density level of the foreign matter 7 becomes lower than that of the surroundings, so that this can be detected.

Then, the result of this determination is used as the main CPU 11
It is output to i (SP5), and the process for one captured image ends.

The seventh measuring unit 11g is shown in FIG.
As shown in the flowchart of FIG.
It is monitored whether or not image data and a processing command are input from j (SQ1). When this is input, density conversion processing is performed on the input image data (SQ2). In this density conversion processing, density conversion is performed using conversion formulas such as linear conversion, quadratic curve conversion, cubic curve conversion, gamma curve conversion, and log curve conversion. As a result, the noise component is reduced and the extracted portion has a uniform density.

After that, the measuring unit 11g monitors whether or not the image data read command is input from the hardware unit 11j (SQ3), and when the read command is input, the density conversion process is performed in the process of SQ2. The image data is output to the hardware unit 11j (SQ4), and the process proceeds to SQ3.

If no read command is input,
It is determined whether or not the image data and the processing command are input from the hard unit 11j (SQ5). If they are not input, the process proceeds to the process of SQ3. If they are input, the binary image data is input. Conversion processing is performed (SQ6).
In this binarization process, each pixel density is divided by a predetermined threshold value and expressed as a light and dark binary value. Next, correction processing is performed in the order of deletion, expansion, contraction, deletion, expansion, and thinning (SQ
7) Then, the image data thus obtained is compared with the reference data input in advance, and a determination process as to whether or not the breaker 4 is defective is performed (SQ8).

Here, when the breaker 4 is defective, the wire cord 4a and rubber are folded,
Since the density level is lower than that of the grid portion of the normal wire cord 4a, this can be detected.

Then, the result of this determination is sent to the main CPU 11
It is output to i (SQ9), and the process for one captured image ends.

The eighth measuring unit 11h is shown in FIG.
As shown in the flowchart of FIG.
It is monitored whether or not the image data and the processing command are input from j (SR1). When these are input, the input image data is binarized (SR2). In this binarization process, each pixel density is divided by a predetermined threshold value and expressed as a light and dark binary value. Next, correction processing such as deletion, expansion, and thinning is performed (SR3), and the image data obtained by this is compared with the reference data that has been input in advance, so that the edges of the breaker 4 are misaligned and overlapped. It is determined whether there is any (SR4).

In this case, the concentration level changes rapidly along the edge of the breaker 4, or the breaker 4
These states are detected because there is no change in density level in two steps in the width direction.

Then, the result of this determination is sent to the main CPU 11
It is output to i (SR5), and the process for one captured image ends.

In this embodiment, the image signal is directly input from the X-ray camera 22 to the apparatus body 10. However, the image signal obtained by the X-ray camera 22 is temporarily stored in a video tape recorder or the like. The same effect can be obtained by recording a picture and inputting an image signal from the video tape recorder to the apparatus body 10.

The first and second embodiments are examples, and the present invention is not limited to these.

[0193]

As described above, according to the tire inspection method of the first aspect of the present invention, the image data in which the contrast is increased and the noise component is removed by the preprocessing is obtained, and the density conversion processing is further performed. And the contrast of the image is further increased by the filter processing and the noise component is further removed, and the image data obtained by the filter processing is binarized, and the binarized image data and predetermined reference data are Are compared with each other to judge whether the internal structure of the tire is good or bad. Therefore, it is possible to obtain a highly accurate result as compared with the inspection and judgment by the human visual inspection, and it is possible to easily automate the inspection work.

According to the tire inspection method of the second aspect, the image data in which the contrast is increased and the noise component is removed by the preprocessing is obtained, and the density conversion processing is further performed, and then the filter processing is performed. The pixel density is averaged within a predetermined area of the image data by the unit by the unit, for example, the contrast of the image is further increased and the noise component is further removed, and the image data obtained by the filtering is It is binarized, and the binarized image data is compared with predetermined reference data to detect the disorder of the end code of the breaker of the tire, and the quality of the structure is determined. High-precision results are obtained compared to inspection judgment,
The inspection work can be easily automated.

Further, according to the tire inspection method of the third aspect, the image data in which the contrast is increased and the noise component is removed by the preprocessing is obtained, and the density conversion processing is further performed, and then the filter processing is performed. The pixel density is averaged within a predetermined area of the image data by the unit by the unit, for example, the contrast of the image is further increased and the noise component is further removed, and the image data obtained by the filtering is It is binarized, and the binarized image data is compared with predetermined reference data to detect the protrusion of the end code of the tire breaker, and the quality of the structure is determined. It is possible to obtain a highly accurate result as compared with the inspection determination and to easily automate the inspection work.

According to the tire inspection method of the fourth aspect, the image data in which the contrast is increased and the noise component is removed by the pre-processing is obtained, and the density conversion processing is further performed, and then the filter processing is performed. The pixel density is averaged in a predetermined area of the image data as a unit, and the differential processing is performed in the predetermined area as a unit to increase, for example, the contrast of an image and noise components. After further removal and emphasizing the portion where the pixel density is greatly changed, the image data obtained by the filtering is binarized, and the binarized image data is compared with predetermined reference data. Then, the opening of the seam of the breaker of the tire is detected, and the quality of the structure is judged, so compared to the inspection judgment by human visual inspection. With the accuracy of the results,
The inspection work can be easily automated.

According to the tire inspection method of the fifth aspect, the image data in which the contrast is increased and the noise component is removed by the preprocessing is obtained, and the density conversion processing is further performed, and then the filter processing is performed. By a differentiating process in a predetermined area of the image data as a unit by, for example, the contrast of the image is increased and the noise component is further removed, and the portion where the pixel density is largely changed is emphasized, The image data obtained by the filter processing is binarized, the binarized image data is compared with predetermined reference data, the overlap of the seam of the tire breaker is detected, and the quality of the structure is determined. As a result, it is possible to obtain a highly accurate result as compared with the inspection judgment by human visual inspection, and it is possible to easily automate the inspection work.

According to the tire inspection method of the sixth aspect, the image data in which the contrast is increased and the noise component is removed by the preprocessing is obtained, and the density conversion processing is further performed, and then the filter processing is performed. The image data obtained by the filtering process is binarized by performing a differentiating process within a predetermined region of the image data as a unit, for example, emphasizing a portion where the pixel density greatly changes. The imaged data and the predetermined reference data are compared, the end code of the breaker of the tire is broken, the break or the arrangement of two breakers is reversed, and the quality of the structure is determined. It is possible to obtain a highly accurate result as compared with the inspection judgment by human eyes, and it is possible to easily automate the inspection work.

According to the tire inspection method of the seventh aspect, the image data in which the contrast is increased and the noise component is removed by the preprocessing is obtained, and the density conversion processing is further performed, and then the filter processing is performed. The pixel density is averaged within a predetermined area of the image data by the unit by the unit, for example, the contrast of the image is further increased and the noise component is further removed, and the image data obtained by the filtering is It is binarized, and the binarized image data and predetermined reference data are compared to detect foreign matter mixed in the breaker placement portion of the tire, and the quality of the structure is determined. It is possible to obtain a highly accurate result as compared with the inspection determination by, and it is possible to easily automate the inspection work.

According to the tire inspection method of the eighth aspect, the image data in which the contrast is increased and the noise component is removed by the preprocessing is obtained, and the density conversion processing is further performed, and then the filter processing is performed. After a differentiating process is performed in a predetermined region of the image data as a unit, a predetermined arithmetic process is performed between the image data and the image data after the density conversion, and after the arithmetic process. After the density conversion processing is performed again on the image data of, the image contrast is further increased, the noise component is further removed, and the portion where the pixel density is greatly changed is emphasized, and then the filter processing is performed. The obtained image data is binarized, and the binarized image data is compared with predetermined reference data, and the tire breaker is defective. Is detected, the quality of the structure is determined, in comparison with the test determination by human visual with high accuracy result is obtained, it is possible to easily achieve automation of inspection work.

According to the tire inspection method of the ninth aspect, the image data from which the contrast is increased and the noise component is removed by the preprocessing is obtained, and the density conversion processing is further performed, and then the filter processing is performed. The pixel density is averaged in a predetermined area of the image data as a unit, and the differential processing is performed in the predetermined area as a unit to increase, for example, the contrast of an image and noise components. After further removal and emphasizing the portion where the pixel density is greatly changed, the image data obtained by the filtering is binarized, and the binarized image data is compared with predetermined reference data. Then, the misalignment or overlap of the ends of the breaker of the tire is detected, and the quality of the structure is determined. Compared to the inspection determination with high accuracy of results, it is possible to easily achieve automation of inspection work.

Further, according to the tire inspection apparatus of the tenth aspect, image data in which the code of the tire breaker and the part other than the code can be clearly distinguished are obtained, and the binarized image is obtained by the judging means. Since the quality of the internal structure of the tire is determined by comparing the data with predetermined reference data and the determination result is output by the output means, a highly accurate result can be obtained as compared with the inspection determination by human visual inspection. At the same time, the work efficiency can be improved by automating the inspection work.

Further, according to the tire inspection apparatus of the eleventh aspect, the image data in which the code of the tire breaker and the part other than the code can be clearly distinguished based on the image data input by the image data input means. Obtained, by the determination means, the binarized image data and predetermined reference data are compared to determine the quality of the internal structure of the tire, and the determination result is output by the output means. A highly accurate result can be obtained as compared with the visual inspection determination, and the work efficiency can be improved by automating the inspection work.

According to the tire inspection apparatus of the twelfth aspect, in addition to the above effects, in the filter processing, the averaging processing unit makes the pixel density within a predetermined area of the image data as a unit. Since the averaging is performed, noise components can be further removed and highly accurate determination can be performed.

According to the tire inspection apparatus of the thirteenth aspect, in addition to the above effect, the breaker end position in the binarized image data is compared with the breaker end position in the reference data, The irregularity of the end cord of the tire breaker is detected, and the quality of the structure is judged, so that a highly accurate result can be obtained as compared with the inspection judgment by human eyes, and the inspection work can be easily automated. it can.

According to the tire inspection apparatus of the fourteenth aspect, in addition to the above effects, the reference data, that is, the image data in the normal state is subtracted from the binarized image data,
Image data in which the pixels of the code protruding from the end of the breaker are clear is obtained, and the protrusion of the code from the end of the breaker is detected based on the image data, and the quality of the structure is determined. It is possible to obtain a highly accurate result as compared with the visual inspection determination, and it is possible to easily automate the inspection work.

Further, according to the tire inspection apparatus of the fifteenth aspect, in addition to the above effects, the binarized image data is compared with the reference data, that is, the image data at the normal time, and the breaker arranging section is provided. After the pixels of the foreign matter mixed into the breaker are clarified, the contamination of the foreign matter into the breaker arrangement portion is detected based on this image data, so a highly accurate result can be obtained as compared with the inspection judgment by human visual inspection. At the same time, the inspection work can be easily automated.

According to the tire inspection apparatus of the sixteenth aspect, in addition to the above effects, in the filter processing, the averaging processing means makes the pixel density within a predetermined area of the image data as a unit. Is averaged, and the differential processing unit performs the differential processing in the predetermined area as a unit, so that the noise component is further removed and the portion where the pixel density greatly changes is emphasized. Therefore, highly accurate determination can be performed.

According to the tire inspection apparatus of the seventeenth aspect, in addition to the above effects, each pixel density of the binarized image data and the reference data, that is, each pixel density of the image data in the normal state. And the opening of the seam of the breaker is detected, the result of higher accuracy can be obtained as compared with the inspection judgment by human eyes, and the inspection work can be easily automated.

Further, according to the tire inspection apparatus of the eighteenth aspect, in addition to the above effects, each pixel density of the binarized image data and the reference data, that is, each pixel density of the image data at the time of normal operation. And the overlap of the breaker seam is detected, a highly accurate result can be obtained as compared with the inspection judgment by human visual inspection, and the inspection work can be easily automated.

Further, according to the tire inspection apparatus of the nineteenth aspect, in addition to the above effects, in the filter processing, the density averaging processing means makes a pixel within a predetermined area of the image data as a unit. Since the density is averaged, it is possible to further remove the noise component, obtain image data in which the code of the tire breaker and the portion other than the code can be clearly distinguished, and perform highly accurate determination.

Further, according to the tire inspection apparatus of the twentieth aspect, in addition to the above effects, the binarized image data and the reference data, that is, the image data at the normal time are compared, and each of the breakers is compared. The break, end, and reverse of the end cord of the breaker are detected based on the detection position of the cord, so that a highly accurate result can be obtained as compared with the inspection judgment by human eyes, and the inspection work can be automated. It can be easily achieved.

According to the tire inspection apparatus of the twenty-first aspect, in addition to the above effects, in the filter processing, the differential processing unit performs the differential processing within the predetermined area of the image data as a unit. The second density conversion is performed after the predetermined calculation processing is performed by the calculation processing means between the image data subjected to the differentiation processing and the density conversion image data by the density conversion processing means. Since the image data after the arithmetic processing is again subjected to the density conversion processing by the processing means, the noise component can be further removed and the code of the tire breaker and the part other than the code can be clearly distinguished. Image data can be obtained and highly accurate determination can be performed.

Further, according to the tire inspection device of the twenty-second aspect, in addition to the above effects, the binarized image data and the reference data are compared, and the cord and the rubber are folded at the breaker. Since such defects are detected, a highly accurate result can be obtained as compared with the inspection judgment by human visual inspection, and the inspection work can be easily automated.

According to the tire inspection apparatus of the twenty-third aspect, in addition to the above effects, the binarized image data and the reference data are compared with each other, whereby the end portions of the breakers are misaligned or aligned. Since the overlap is detected, it is possible to obtain a highly accurate result as compared with the inspection judgment by human visual inspection, and it is possible to easily automate the inspection work.

Further, according to the tire inspection device of the twenty-fourth aspect, the first to eighth determination means can simultaneously make the determinations regarding different tire inspection items, so that the tire inspection can be performed in a short time. In addition to being able to be performed, a highly accurate result can be obtained as compared with the inspection judgment by the human visual inspection. As a result, the inspection work can be easily automated. Further, since the common processing for obtaining the image data necessary for the first to eighth determinations is time-divisionally performed by the filter processing means, the configuration can be simplified.

Further, according to the tire inspection apparatus of the twenty-fifth aspect, in addition to the above effects, the filter processing means is composed of one piece of hardware and performs the filter processing in a time division manner. Therefore, it is possible to significantly speed up the process and shorten the tire inspection time.

[Brief description of the drawings]

FIG. 1 is a block diagram showing a tire inspection device according to a first embodiment of the present invention.

FIG. 2 is a diagram showing a tire configuration.

FIG. 3 is a diagram showing a tire breaker.

FIG. 4 is a diagram showing an arrangement of tire breakers.

FIG. 5 is a view showing a state where the end portions of the tire breaker are aligned and overlapped.

FIG. 6 is a view showing a state in which the ends of the tire breaker are arranged in reverse order.

FIG. 7 is a diagram showing a state in which the seams of the tire breakers overlap.

FIG. 8 is a view showing a state where the seam of the tire breaker is opened.

FIG. 9 is a view showing a state in which the ends of the tire breaker are misaligned.

FIG. 10 is a view showing a state where the end cord of the tire breaker is broken.

FIG. 11 is a diagram showing a state in which the end cords of the tire breaker are disturbed.

FIG. 12 is a diagram showing a state where the end cords of the tire breaker are scattered.

FIG. 13 is a view showing a state where the end cord of the tire breaker is protruding.

FIG. 14 is a diagram showing a defective state of the tire breaker.

FIG. 15 is a diagram showing a state in which foreign matter is mixed in the tire breaker.

FIG. 16 is a flowchart showing processing of a preprocessing unit according to the first embodiment of the present invention.

FIG. 17 is a flowchart showing the processing of the measurement unit according to the first embodiment of the present invention.

FIG. 18 is a flowchart showing the processing of the measurement unit according to the first embodiment of the present invention.

FIG. 19 is a flowchart showing processing of the measurement unit according to the first embodiment of the present invention.

FIG. 20 is a flowchart showing processing of the measurement unit according to the first embodiment of the present invention.

FIG. 21 is a flowchart showing the processing of the measurement unit according to the first embodiment of the present invention.

FIG. 22 is a flowchart showing processing of the measurement unit according to the first embodiment of the present invention.

FIG. 23 is a flowchart showing the processing of the measurement unit according to the first embodiment of the present invention.

FIG. 24 is a flowchart showing the processing of the measurement unit according to the first embodiment of the present invention.

FIG. 25 is a block diagram showing a tire inspection device according to a second embodiment of the present invention.

FIG. 26 is a flowchart showing processing of the hard unit according to the second embodiment of the present invention.

FIG. 27 is a flowchart showing processing of the hard unit according to the second embodiment of the present invention.

FIG. 28 is a flowchart showing the processing of the hard unit according to the second embodiment of the present invention.

FIG. 29 is a flowchart showing the processing of the measurement unit according to the second embodiment of the present invention.

FIG. 30 is a flowchart showing processing of the measurement unit according to the second embodiment of the present invention.

FIG. 31 is a flowchart showing the processing of the measurement unit according to the second embodiment of the present invention.

FIG. 32 is a flowchart showing the processing of the measurement unit according to the second embodiment of the present invention.

FIG. 33 is a flowchart showing the processing of the measurement unit according to the second embodiment of the present invention.

FIG. 34 is a flowchart showing the processing of the measurement unit according to the second embodiment of the present invention.

FIG. 35 is a flowchart showing the processing of the measurement unit according to the second embodiment of the present invention.

FIG. 36 is a flowchart showing the processing of the measurement unit according to the second embodiment of the present invention.

[Explanation of symbols]

1 ... Tire, 2 ... Tread, 2a ... Crown part, 2b ...
Shoulder part, 2c ... Sidewall part, 2d ... Rim cushion part, 3 ... Carcass, 4 ... Breaker, 4a ... Wire cord, 5 ... Bead, 6 ... Rim, 7 ... Foreign matter, 10
... Main body of tire inspection device, 11 ... Image measuring unit, 11a to 1
1h ... measuring unit, 11i ... main CPU, 11j ...
Hardware unit, 12 ... Pre-processing unit, 12a ... Emphasis processing unit, 12b ... Average addition processing unit, 12c-12d ... Image memory, 13 ... Display switching unit, 14 ... Display unit, 15 ... Storage unit, 16 ... Data input unit , 17 ... keyboard, 18 ... video printer, 19 ... data display monitor.

Claims (25)

[Claims] 1. An image of two breakers, which are provided between a carcass and a tread of a tire and have a plurality of cords arranged in different directions at predetermined intervals, are imaged by using electromagnetic radiation. A tire inspection method for inspecting the internal structure of a tire based on image data, wherein after enhancing the brightness of image data at the same imaging position, a plurality of image data at the same imaging position are added, and the addition result is added After performing a pre-processing for averaging the image data by dividing by, a predetermined density conversion process is performed on the image data, and a predetermined filter process is performed on the image data subjected to the density conversion process. The image data obtained by the filtering is binarized, and the binarized image data is compared with predetermined reference data to determine the internal structure of the tire. Tire inspection method characterized by determining the. 2. The filtering process averages pixel densities in a predetermined area of the image data in the area, and compares the binarized image data with predetermined reference data to obtain the image data. The tire inspection method according to claim 1, wherein a disorder of the end cord of the breaker is detected. 3. In the filtering process, the pixel density is averaged in a predetermined area of the image data as a unit, and the binarized image data is compared with predetermined reference data to obtain the image data. The tire inspection method according to claim 1, wherein a jump out of an end cord of the breaker is detected. 4. In the filtering process, the pixel density is averaged in a predetermined region of the image data in the unit, and a differentiation process is performed in the unit in the predetermined region to perform the binarization. The tire inspection method according to claim 1, wherein the opening of the seam of the breaker is detected by comparing the image data and predetermined reference data. 5. In the filtering process, a predetermined region of the image data is used as a unit and a differentiation process is performed within the region, and the binarized image data and predetermined reference data are compared to each other, whereby a seam of the breaker is obtained. 2. The tire inspection method according to claim 1, wherein the overlapping of the two is detected. 6. The edge of the breaker is obtained by performing a differentiating process in a predetermined area of the image data as a unit in the filter processing and comparing the binarized image data with predetermined reference data. 2. The tire inspection method according to claim 1, wherein breakage, breakage, or reverse arrangement of the two breakers is detected. 7. The filtering process averages pixel densities within a predetermined area of the image data in the area, and compares the binarized image data with predetermined reference data to obtain the image data. The tire inspection method according to claim 1, wherein foreign matter is mixed into the breaker arrangement portion. 8. In the filtering process, after performing a differentiation process within a predetermined region of the image data as a unit, a predetermined calculation process is performed between the image data and the image data after the density conversion. At the same time, density conversion processing is performed again on the image data after the arithmetic processing, and the defect of the breaker is detected by comparing the binarized image data with predetermined reference data. The tire inspection method according to claim 1. 9. In the filtering process, the pixel density is averaged in a predetermined region of the image data as a unit, and the differential process is performed in the region as a unit of the predetermined region to perform the binarization. 2. The tire inspection method according to claim 1, wherein a misalignment or an overlap of the end portions of the breaker is detected by comparing the image data and predetermined reference data. 10. An arrangement of two breakers, which are provided between a carcass and a tread of a tire and have a plurality of cords arranged in different directions at predetermined intervals, are obtained by imaging using electromagnetic radiation. A tire inspection device for inspecting an internal structure of a tire based on image data, comprising: a radiation source for irradiating a tire to be inspected with electromagnetic radiation such as X-rays or γ-rays; and a predetermined imaging target position in the tire. At the same imaging position, by inputting the electromagnetic radiation that has passed through the detector and detecting the energy of the electromagnetic radiation, and by making the energy amount of the electromagnetic radiation detected by the detector correspond to the coordinates of the imaging target position as image data. Image enhancement means that enhances the image data of and the brightness is improved, and after adding the image data of the same imaging position, the addition result is added Averaged image data storage means for storing averaged image data divided by the number of times, density conversion processing means for performing a predetermined density conversion processing on the averaged image data, and image data subjected to the density conversion processing Filter processing means for subjecting the image data obtained by the filter processing to binarization, and comparing the binarized image data with predetermined reference data. A tire inspection apparatus comprising: a determination unit that determines whether the internal structure of the tire is good or bad by doing so; and an output unit that outputs a determination result of the determination unit. 11. An image obtained by imaging, using electromagnetic radiation, an arrangement of two breakers provided between a carcass and a tread of a tire and having a plurality of cords arranged in different directions at predetermined intervals. A tire inspection device for inspecting an internal structure of a tire based on image data, comprising image data input means for inputting the image data, and image data input by the image data input means at the same image pickup position. And an averaged image data storage unit for storing averaged image data obtained by adding the image data at the same imaging position and dividing the addition result by the number of additions, and the averaged image data Density conversion processing means for performing predetermined density conversion processing, and filter processing for performing predetermined filter processing on the image data subjected to the density conversion processing. Means, binarizing means for binarizing the image data obtained by the filtering, and comparing the binarized image data with predetermined reference data to determine the quality of the internal structure of the tire. A tire inspection apparatus comprising: a determination unit for determining and an output unit for outputting a determination result by the determination unit. 12. The filter processing means comprises an averaging processing means for averaging pixel densities within a predetermined area of the image data as a unit. Tire inspection equipment. 13. The determination means compares the breaker end position in the binarized image data with the breaker end position in the reference data to detect a disturbance at the end of the breaker. Claim 1 characterized by
2. The tire inspection device described in 2. 14. The judging means subtracts the reference data from the binarized image data, and detects a jump of a code from an end of the breaker based on the image data of the subtraction result. The tire inspection device according to claim 12, wherein the tire inspection device is a tire inspection device. 15. The determination process detects contamination of a foreign substance into an arrangement portion of the breaker by comparing the binarized image data with the reference data. Tire inspection device described. 16. The filter processing means performs averaging processing means for averaging pixel densities in a predetermined area of the image data in a unit, and differential processing in the area in a predetermined area. The tire inspection apparatus according to claim 10 or 11, further comprising a differential processing unit. 17. The determination unit detects the opening of the seam of the breaker by comparing each pixel density of the binarized image data with each pixel density of the reference data. The tire detection device according to claim 16. 18. The determination means detects the overlap of the breaker seam by comparing each pixel density of the binarized image data with each pixel density of the reference data. The tire detection device according to claim 16. 19. The tire inspection apparatus according to claim 10, wherein the filter processing unit includes a differential processing unit that performs a differential process within a predetermined area of the image data as a unit. . 20. The judging means compares the binarized image data with the reference data, and detects breaks, dislocations, and reverse arrangements of the end code of the breaker. Item 19. The tire inspection device according to item 19. 21. The filter processing means performs a differential processing in a predetermined area of the image data as a unit in the area, the differential processed image data and density conversion by the density conversion processing means. And a second density conversion processing means for performing density conversion processing again on the image data after the arithmetic processing. The tire inspection device according to claim 10 or 11. 22. The tire inspection apparatus according to claim 21, wherein the determination unit compares the binarized image data with the reference data to detect a defect in the breaker. 23. The determination means compares the binarized image data with the reference data to detect misalignment or overlap of end portions of the breaker. Tire inspection equipment. 24. An image of an arrangement of two breakers provided between a carcass and a tread of a tire, in which a plurality of cords are provided side by side at predetermined intervals in different directions, is obtained by imaging using electromagnetic radiation. A tire inspection device for inspecting an internal structure of a tire based on image data, comprising image data input means for inputting the image data, and image data input by the image data input means at the same image pickup position. And an averaged image data storage unit for storing averaged image data obtained by adding the image data at the same imaging position and dividing the addition result by the number of additions, and the averaged image data Density conversion processing means for performing a predetermined density conversion processing, and a predetermined area of the image data as a unit for the image data subjected to the density conversion processing. Averaging processing for averaging pixel densities within the area, differential processing for performing differential processing within the area in units of a predetermined area of the image data, image data subjected to the differential processing, and the density conversion processing means A filtering process for performing time-division of each of a calculation process for performing a predetermined calculation with the image data after the density conversion by the above, and a second density conversion process for performing the density conversion process again on the image data after the calculation process. Means, binarizing means for binarizing the image data obtained by the filtering, and comparing the breaker end position in the binarized image data with the breaker end position in the reference data. A first determining means for detecting a disturbance at the end of the breaker; subtracting the reference data from the binarized image data to obtain image data of the subtraction result. Second determination means for detecting the jumping out of the code from the end of the breaker, by comparing each pixel density of the binarized image data with each pixel density of the reference data, Third judgment means for detecting the opening of the breaker seam, and comparing each pixel density of the binarized image data with each pixel density of the reference data, thereby overlapping the breaker seam. And a fifth determining means for comparing the binarized image data with the reference data to detect bending, dislocation, and reverse arrangement of the end code of the breaker. A second determination unit that detects foreign matter mixed into the arrangement portion of the breaker by comparing the binarized image data with the reference data; and the binarized image data. Seventh determination means for comparing the reference data with each other to detect a defect of the breaker, and comparing the binarized image data with the reference data, misalignment or overlap of the end portions of the breaker. A tire inspection apparatus comprising: an eighth determination means for detecting the above, and an output means for outputting a determination result by the determination means. 25. The tire inspection apparatus according to claim 24, wherein the filter processing unit is composed of one piece of hardware, and performs the filter processing in a time division manner.