CN116818785B - Defect detection method, system and medium based on machine vision - Google Patents
Defect detection method, system and medium based on machine vision Download PDFInfo
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- G—PHYSICS
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
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- G01N21/88—Investigating the presence of flaws or contamination
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- G01N21/892—Investigating the presence of flaws or contamination in moving material, e.g. running paper or textiles characterised by the flaw, defect or object feature examined
- G01N21/894—Pinholes
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- G—PHYSICS
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8806—Specially adapted optical and illumination features
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
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- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
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Abstract
The embodiment of the application discloses a defect detection method, a defect detection system and a defect detection medium based on machine vision. The method comprises the following steps: illuminating a first surface of a material in a production line with first brightness through a light source, and acquiring an image of a second surface of the material corresponding to an illumination area of the light source through a line scanning camera to obtain a first image; the first surface is opposite to the second surface; illuminating a first surface of a material in the production line with a second brightness through a light source, and acquiring an image of a second surface of the material corresponding to an illumination area of the light source through a line scanning camera to obtain a second image; wherein the first brightness is different from the first brightness; and detecting defects of the material according to the first image and the second image. The scheme can accurately distinguish and detect the first defect and the second defect, avoids the situation that the first defect and the second defect are confused and are difficult to distinguish, and realizes accurate detection of the defects.
Description
Technical Field
The present application relates to the field of machine vision, and in particular, to a defect detection method, system, and medium based on machine vision.
Background
In the production process of materials, pinholes appear on the materials due to process or machine reasons, the diameters of the pinholes are about 0.1mm, even smaller, and the production line cannot be identified manually in the high-speed production process. And pinholes can have a mechanical impact on the operation of the equipment made of the material. For example, in the case of a lithium battery separator, once a pinhole is formed in the separator, it causes a short circuit between the positive and negative electrodes, which affects the battery performance and causes an accident. Meanwhile, other defects such as oil stains, bright spots and the like often occur in the material, and under the traditional transmission polishing mode, pinholes and other defects cannot be distinguished.
At present, in a scheme for distinguishing and detecting pinholes and other defects, pictures of the pinholes and other defects are compared and distinguished. However, when the light source is present, the light source supplements light to other defects, which results in the same high brightness of other defects, and the images of other defects are easily confused with the images of pinholes. If the light source irradiation angle is changed to perform the distinguishing detection, the transmission of the light source is error due to the fact that the surface of the material is not absolute horizontal and has concave-convex, and the normal detection of the defects is affected.
Disclosure of Invention
The application provides a defect detection method, a system and a medium based on machine vision, which are used for accurately distinguishing and detecting a first defect and a second defect.
According to an aspect of the present application, there is provided a machine vision-based defect detection method, the method including:
illuminating a first surface of a material in a production line with first brightness through a light source, and acquiring an image of a second surface of the material corresponding to an illumination area of the light source through a line scanning camera to obtain a first image; wherein the first surface is opposite the second surface;
illuminating a first surface of a material in a production line with a second brightness through a light source, and acquiring an image of a second surface of the material corresponding to an illumination area of the light source through a line scanning camera to obtain a second image; wherein the first luminance is different from the first luminance;
and detecting defects of the material according to the first image and the second image.
According to another aspect of the present application, there is provided a machine vision-based defect detection system, the system comprising:
the conveyor belt is used for driving the material to move;
A line scan camera for image acquisition of the surface of the material in the production line;
the light source is used for irradiating the surface opposite to the surface of the material collected by the line scanning camera;
an electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor;
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the machine vision-based defect detection method of any one of the embodiments of the present application.
According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the machine vision-based defect detection method of any one of the embodiments of the present application when executed.
According to the technical scheme, a first surface of a material in a production line is irradiated by a light source at first brightness, and a second surface of the material corresponding to an irradiation area of the light source is subjected to image acquisition by a line scanning camera, so that a first image is obtained; wherein the first surface is opposite the second surface; illuminating a first surface of a material in a production line with a second brightness through a light source, and acquiring an image of a second surface of the material corresponding to an illumination area of the light source through a line scanning camera to obtain a second image; wherein the first luminance is different from the first luminance; and detecting defects of the material according to the first image and the second image. According to the scheme, defects of materials are detected in two modes by adjusting the brightness of the light source, so that two defects are accurately distinguished, the problem that different defects are difficult to distinguish due to the same brightness under the light supplementing effect is solved, and accurate distinguishing detection of the defects is realized.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the application or to delineate the scope of the application. Other features of the present application will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a defect detection method based on machine vision according to a first embodiment of the present application;
FIG. 2 is a schematic diagram of a dual channel light source according to a first embodiment of the present application;
FIG. 3 is a schematic view of an illumination area provided according to a first embodiment of the present application;
FIG. 4 is a schematic diagram of a defect detection process according to a first embodiment of the present application;
FIG. 5 is a schematic view of a non-transparent plate according to a first embodiment of the present application;
FIG. 6 is a side view of a light source illuminated without a non-transparent plate provided in accordance with an embodiment of the application;
FIG. 7 is a side view of a light source illuminated with a non-transparent plate according to a first embodiment of the present application;
FIG. 8 is a flow chart of a defect detection method based on machine vision according to a second embodiment of the present application;
FIG. 9 is a flow chart of a defect detection method based on machine vision according to a third embodiment of the present application;
fig. 10 is a schematic structural diagram of a defect detecting device based on machine vision according to a fifth embodiment of the present application;
fig. 11 is a schematic structural diagram of an electronic device according to a sixth embodiment of the present application.
Detailed Description
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," "third," "fourth," "actual," "preset," and the like in the description and the claims of the present application and in the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a machine vision-based defect detection method according to an embodiment of the present application, which is applicable to detecting defects of materials. The method may be performed by a machine vision-based defect detection device, which may be implemented in hardware and/or software, which may be configured in an electronic apparatus. As shown in fig. 1, the method includes:
s110, irradiating a first surface of a material in a production line with first brightness through a light source, and acquiring an image of a second surface of the material corresponding to an irradiation area of the light source through a line scanning camera to obtain a first image; wherein the first surface is opposite to the second surface.
The light source can be any light source capable of adjusting brightness to obtain at least two kinds of brightness. By way of example, a dual channel light source may be used, as shown in fig. 2. The light source of the shadow part belongs to the light source of the channel 1, and is controlled by the control signal corresponding to the channel 1, and the light source of the non-shadow part belongs to the light source of the channel 2, and is controlled by the control signal corresponding to the channel 2.
In a production line, a conveyor belt may be provided, on which the material is placed, and which moves the material. The first surface may be a surface of the material opposite to the surface to be defect detected, and the surface to be defect detected may be a second surface. The line scan camera is a camera employing a line image sensor. The line scanning camera performs moving scanning along one direction from pixel point to pixel point in one scanning period, and repeats scanning actions in different scanning periods.
In the embodiment of the application, the irradiation direction of the light source and the image acquisition direction of the line scanning camera are determined by the setting direction of the material, if the material is tiled on a conveyor belt of a production line, the lower surface can be used as a first surface, the light source is controlled to irradiate the first surface with first brightness below the material, the upper surface is used as a second surface, and the line scanning camera is controlled to acquire the image of a part, corresponding to the irradiation area of the light source, of the second surface with second brightness above the material. As shown in fig. 3, fig. 3 is a top view of the material. Assuming that the light source irradiation area is a circular area in fig. 3, the line scanning camera performs image acquisition on an area corresponding to the circular area in a second surface opposite to the first surface irradiated by the light source. If the upper surface of the belt is a first surface and the lower surface is a second surface, the light source may be disposed above the material to illuminate the first surface with a first brightness, and the line scan camera may be disposed below the material to collect an image of an area corresponding to the illuminated area of the second surface and the light source with a second brightness. The material can also move along the direction vertical to the ground, and the positions and the working directions of the light source and the line scanning camera can be synchronously changed.
S120, irradiating a first surface of a material in a production line with a second brightness through a light source, and acquiring an image of a second surface of the material corresponding to an irradiation area of the light source through a line scanning camera to obtain a second image; wherein the first luminance is different from the first luminance.
For example, during one scan cycle of the line scan camera, the light source may be adjusted to a first brightness, the first surface of the material in the production line is illuminated by the light source at the first brightness, and the second surface is image captured by the line scan camera. In the next scanning period of the line scanning camera, the light source can be adjusted to a second brightness, the second surface of the material in the production line is irradiated by the light source at the second brightness, and the image of the second surface is acquired by the line scanning camera. The first luminance may be set to be smaller than the second luminance, or the first luminance may be set to be larger than the second luminance.
For example, as shown in fig. 2, if the first brightness is set to be less than the second brightness, the light source corresponding to the channel 1 may be controlled to be turned on to obtain the light source with the first brightness, and then the light sources of the channel 1 and the channel 2 are controlled to be turned on to obtain the light source with the second brightness.
In the embodiment of the application, the line scanning camera can be set to be in a stroboscopic mode, which comprises two scanning time periods, and a stroboscopic trigger signal is sent to the light source controller, and the light source controller controls the light source to switch the brightness. For example, in one scanning period of the line scanning camera, a strobe trigger signal is sent to the light source controller, the light source controller controls the light source to be adjusted to a first brightness, in the next scanning period of the line scanning camera, a strobe trigger signal is sent to the light source controller, the light source controller controls the light source to be adjusted to a second brightness, in the next scanning period of the line scanning camera, a strobe trigger signal is sent to the light source controller, the light source controller controls the light source to be adjusted to a first brightness, in the next scanning period of the line scanning camera, a strobe trigger signal is sent to the light source controller, the light source controller controls the light source to be adjusted to a second brightness, and so on, the above-described process is cyclically executed.
In an embodiment of the present application, image acquisition is performed on a second surface of the material corresponding to the light source irradiation area by using a line scanning camera, including:
scanning the second surface of the material corresponding to the light source irradiation area by the line scanning camera along the first direction pixel by pixel in one scanning period of the line scanning camera; wherein the first direction is the perpendicular direction of the movement direction of the material in the production line;
Scanning a second surface of the material corresponding to the light source irradiation area in a second direction by the line scanning camera in different scanning periods of the line scanning camera; wherein the second direction is the movement direction of the material in the production line.
Illustratively, as shown in fig. 4, the line scan camera scans and image captures pixel by pixel from one end of the material to the other end in a first direction during one scan cycle. Since the material is moving in the second direction, this corresponds to scanning the material in the opposite direction of the second direction by the line scan camera during different scanning cycles.
In the embodiment of the application, the irradiation direction of the light source is provided with a non-transparent plate with a gap, the gap width of the non-transparent plate is determined according to the pixel point width of the line scanning camera, and the gap area of the non-transparent plate is determined according to the scanning area of the line scanning camera in the scanning period.
For example, in order to avoid that excessive light of the light source generates light compensation to the light-tight defect of the material surface, light of an area which cannot be scanned by the line scanning camera can be shielded, so that light compensation is avoided. For example, a non-transparent plate with a slit may be provided in the irradiation direction of the light source, and the slit width of the non-transparent plate may be equal to the pixel width of the line scanning camera, or the difference between the slit width and the pixel width of the line scanning camera may be smaller than a preset difference. The slit corresponds to a scanning area of the line scanning camera in one scanning period, so that light of the light source can pass through the slit and enter the line scanning camera through the light-transmitting area in the scanning process of the line scanning camera. The non-transparent plate can be made of specially-made steel plate or other metal plate or non-metal plate, and is opaque.
Illustratively, as shown in FIG. 5, FIG. 5 is a top view of a non-transparent plate through which light from a light source passes from below the non-transparent plate through a slit, and through which light enters a line scan camera if there is a light transmission defect in the material. The arrangement direction of the slit is identical to the first direction in fig. 4, and the slit coincides with the scanning area of the line scanning camera in one cycle. As shown in fig. 6, fig. 6 is a side view of light source irradiation without a non-transparent plate, if there is no shielding of the non-transparent plate, for a pixel point scanned by a line scanning camera, if there is a light transmission defect, light rays corresponding to a plurality of pixel points on the light source all pass through the light transmission defect, and light supplement is generated. As shown in fig. 7, fig. 7 is a side view of light source irradiation when a non-transparent plate exists, if there is a light transmission defect in the number of times, only a light ray corresponding to one pixel point passes through a light transmission area due to shielding of the non-transparent plate, and no light supplement is generated, so that the situation that the light supplement causes the non-light transmission defect to be brighter is avoided, a highlight image collected by a line scanning camera is easy to be confused with a highlight image of the light transmission defect, and the light supplement to the non-light transmission defect is avoided through shading of the non-transparent plate, so that the detection accuracy is improved.
S130, detecting defects of the material according to the first image and the second image.
For example, since the first luminance is different from the second luminance, for a defect affecting the luminance with respect to the light supplement, the defect may be acquired by the line scan camera to present a different image in the two acquired images. For pinhole type determination, since the light source enters the line scanning camera through the pinhole, the line scanning camera has high brightness, so that the pinhole forms the same high brightness image at the imaging position in the two acquired images. Therefore, the first image and the second image can be subjected to gray scale detection, and the defect that the light filling does not affect the brightness and the defect that the light filling affects the brightness can be distinguished by the gray scale difference. If the first defect is a defect that the light filling does not affect the brightness, and the second defect is a defect that the light filling affects the brightness, determining the defect with unchanged brightness in the first image and the second image as the first defect, and taking the defect with changed brightness as the second defect.
According to the technical scheme, a first surface of a material in a production line is irradiated by a light source at first brightness, and a second surface of the material corresponding to an irradiation area of the light source is subjected to image acquisition by a line scanning camera, so that a first image is obtained; wherein the first surface is opposite the second surface; illuminating a first surface of a material in a production line with a second brightness through a light source, and acquiring an image of a second surface of the material corresponding to an illumination area of the light source through a line scanning camera to obtain a second image; wherein the first luminance is different from the first luminance; and detecting defects of the material according to the first image and the second image. According to the scheme, defects of materials are detected in two modes by adjusting the brightness of the light source, so that two defects are accurately distinguished, the problem that different defects are difficult to distinguish due to the same brightness under the light supplementing effect is solved, and accurate distinguishing detection of the defects is realized.
Example two
Fig. 8 is a flowchart of a defect detection method based on machine vision according to a second embodiment of the present application, which is optimized based on the above embodiment, and a scheme not described in detail in the embodiment of the present application is shown in the above embodiment. As shown in fig. 8, the method in the embodiment of the present application specifically includes the following steps:
s210, irradiating a first surface of a material in a production line with first brightness through a light source, and acquiring an image of a second surface of the material corresponding to an irradiation area of the light source through a line scanning camera to obtain a first image; wherein the first surface is opposite to the second surface.
S220, irradiating the first surface of the material in the production line with a second brightness through a light source, and acquiring an image of the second surface of the material corresponding to the irradiation area of the light source through a line scanning camera to obtain a second image; wherein the first brightness is less than the second brightness.
In the embodiment of the present application, the first luminance may be set smaller than the second luminance. The first surface of the material may be irradiated by the light source, and the gray value of the pixel in the image obtained by the line scanning camera collecting the image of the second surface is a first gray value, the brightness when the gray value of the pixel in the image obtained by collecting the image without the material is a second gray value is taken as the first brightness, the first gray value may be 10, which represents that the image is close to black, and the second gray value may be 255, which represents that the image is close to white. The light source may be configured to use, as the second luminance, a luminance when a pixel gray value in an image obtained by irradiating the first surface of the material with the light source and performing image acquisition on the second surface with the line scan camera is a third gray value, where the third gray value may be 128, and is expressed as a middle gray of black and white.
S230, determining first defects of the material according to the first image, and determining all defects of the material according to the second image.
For example, when the first brightness is lower, the first surface of the material is irradiated by the light source at the first brightness, and the second surface of the material is acquired by the line scan camera, if the surface of the material is free of defects, the light source is shielded by the material, the gray value of the first image acquired by the line scan camera is smaller, and the first image is close to black. If the surface of the material has defects such as oil stains and bright spots, the first brightness is smaller, and no light compensation is generated for the defects, so that the gray values of the oil stains and the brightness displayed in the first image are smaller. If pinholes exist on the surface of the material, the pinholes are penetrated, so that light of the light source can completely penetrate through the pinholes and enter the line scanning camera, and a pinhole image in a first image acquired by the line scanning camera is highlighted and is close to white. Based on this, a first defect of the material may be determined in the first image, and a pixel region close to white in the first image is determined as the first defect, for example, a pixel region having a gray value of 255 in the first image is determined as the first defect.
Because the second brightness is greater than the first brightness, light can be transmitted to the second surface to supplement light to the defect outside the pinhole in the irradiation process, so the defect outside the pinhole in the second image acquired by the line scanning camera may also have higher brightness close to white. The second image may be inspected to identify pixel areas that are nearly white therein as defects, thereby identifying all defects, including pinhole-like through-light defects and other opaque surface defects.
S240, removing the first defect from the total defects to obtain a second defect of the material.
In the embodiment of the application, the first defect obtained from the first image can be removed from all defects obtained from the second image, so as to obtain the second defect of the material. Specifically, the pixel areas corresponding to all defects may be marked in the second image, and the pixel areas corresponding to the first defect may be marked in the first image. And removing the pixel region corresponding to the first defect from the pixel regions corresponding to all the defects to obtain the pixel region corresponding to the second defect.
In an embodiment of the present application, if the material does not move with the conveyor belt during one cycle of performing S210-S220, the first image and the second image correspond to the same area of the material. If the material moves with the conveyor belt, there is a deviation in the areas corresponding to the first image and the second image. The distance that the material moves during the execution of S210 may be determined according to the moving speed and moving direction of the material, the first image and the second image are subjected to the alignment process according to the moving distance, and a portion corresponding to the same region of the material therein is determined.
In an embodiment of the present application, determining a first defect of a material according to the first image includes:
if a pixel point range with the gray value larger than a first preset gray value exists in the first image, determining an area corresponding to the pixel point range as the first defect;
determining all defects of the material from the second image, comprising:
and if the pixel point range with the gray value larger than the second preset gray value exists in the second image, determining the area corresponding to the pixel point range as the second defect. Wherein the first preset gray scale is larger than the second preset gray scale
Illustratively, if there is a through light-transmitting defect in the material, then all of the light from the light source passes through the defect into the line scan camera, rendering white in the first image. The first preset gray level may be determined, if a pixel range with a gray level value greater than the first preset gray level exists in the first image, the region corresponding to the pixel range is determined to be the first defect, and the first preset gray level may be 255, or a gray level value less than 255 and a difference value with 255 less than a preset difference value. Because the first brightness is darker, no light supplementing can be carried out on the surface defects of oil stain, bright point and the like which are opaque on the second surface of the material, the brightness displayed in the first image is darker, the gray value is smaller, and based on the first brightness, the first defects can be screened out in the first image, and other defects can be filtered out. The second brightness is brighter, light is supplemented to the defects on the second surface of the material, and a brighter effect is shown in the second image. A second preset gray level may be set, and a region having a gray level value greater than the second preset gray level is determined as a second defect. The second preset gray scale may be 128, or a gray scale value greater than 128 and having a difference from 128 less than the preset difference. In the second image, the defects with the gray value larger than the second preset gray value include defects penetrating through light, such as pinholes, and also include surface defects not penetrating light, such as oil stains, bright spots, mosquitoes and the like.
In the embodiment of the application, the first defect is a light-transmitting defect, the second defect is a light-non-transmitting defect, the first preset gray level is determined according to a gray level value of white, and the second preset gray level is determined according to a gray level value of a material surface acquired by a line scanning camera when the light source irradiates with a second brightness.
The embodiment of the application provides a defect detection method based on machine vision, which comprises the steps of setting first brightness smaller than second brightness, determining first defects of a material according to a first image, and determining all defects of the material according to a second image. And removing the first defect from all the defects to obtain a second defect of the material, wherein the light-supplementing of the opaque defect is not carried out by the low-brightness light source, the light-supplementing of the opaque defect is carried out by the high-brightness light source, different effects are shown by the opaque defect in the first image and the second image, the light rays of the low-brightness light source and the high-brightness light source all penetrate through the transparent defect and enter the line scanning camera, and the highlight effects are shown by the transparent defect in the first image and the second image, so that the first defect and the second defect are accurately distinguished.
Example III
Fig. 9 is a flowchart of a defect detection method based on machine vision according to a third embodiment of the present application, which is optimized based on the above embodiment, and a scheme not described in detail in the embodiment of the present application is shown in the above embodiment. As shown in fig. 9, the method in the embodiment of the present application specifically includes the following steps:
s310, the brightness of a light source is adjusted in the process of irradiating a first surface of a material in a production line through the light source and collecting an image of a second surface of the material corresponding to an irradiation area of the light source through a line scanning camera.
Illustratively, prior to performing S340, the first process pre-performs determining the first brightness and the second brightness. The first surface of the material in the production line is irradiated by the light source with initial brightness, the second surface of the material is subjected to image acquisition by the line scanning camera, and the brightness of the light source is adjusted in the process again until the acquired image reaches the preset condition.
S320, if the average gray level of the collected image is a third preset gray level and the average gray level of the collected image is a first preset gray level when the material is removed, the brightness of the light source under the condition is taken as the first brightness.
The third preset gray level may be determined according to an actual situation, for example, may be set to a lower gray level, so that the first brightness adjusted correspondingly when the material area in the acquired image is the third preset gray level will not generate light filling for the opaque defect on the material surface. The first preset brightness may be determined according to an actual situation, for example, may be determined as a gray value 255 corresponding to white, or less than 255 and a difference from 255 is less than a preset difference.
In the process of adjusting the brightness of the light source, the line scanning camera collects images of the second surface according to preset frequency, and determines average gray values of the collected images. If the average gray level is the fourth preset gray level, determining that the brightness of the light source is lower at the moment, and no light supplementing is generated on the light-tight defect on the surface of the material. However, it is desirable to ensure that the light from the light source with the brightness passes through the light-transmitting defect, and enters the line scanning camera with enough brightness so that the light-transmitting defect appears white in the acquired image, so that the material is removed, the image is acquired by the line scanning camera, and the average gray value in the acquired image is the first gray value. The luminance of the light source when the above conditions are simultaneously satisfied is taken as the first luminance.
S330, if the average gray value of the acquired image is the second preset gray value, the brightness of the light source in this case is taken as the second brightness.
The second preset gray level may be an intermediate gray level of a gray level corresponding to white and a gray level corresponding to black, for example, 128. Under the condition that the second brightness is larger, the light rays can reach the second surface to generate light filling through transmission, so that the material surface presents an effect of higher brightness in the image, therefore, a second preset gray level can be set, if the average gray level of the acquired image is the second preset gray level, the brightness is determined to be adjusted to a higher value, and the brightness of the light source at the moment is determined to be the second brightness.
S340, irradiating a first surface of a material in a production line with a first brightness through a light source, and acquiring an image of a second surface of the material corresponding to an irradiation area of the light source through a line scanning camera to obtain a first image; wherein the first surface is opposite to the second surface.
S350, irradiating the first surface of the material in the production line with a second brightness through a light source, and acquiring an image of the second surface of the material corresponding to the irradiation area of the light source through a line scanning camera to obtain a second image; wherein the first luminance is different from the first luminance.
S360, detecting defects of the material according to the first image and the second image.
The embodiment of the application provides a defect detection method based on machine vision, which comprises the steps of irradiating a first surface of a material in a production line through a light source, adjusting the brightness of the light source in the process of collecting an image of a second surface of the material corresponding to an irradiation area of the light source through a line scanning camera, and taking the brightness of the light source under the condition as the first brightness if the average gray value of the collected image is a third preset gray value and the average gray value of the collected image when the material is removed is a first preset gray value and taking the brightness of the light source under the condition as the second brightness if the average gray value of the collected image is a second preset gray value. By setting the brightness, different defects have a large distinguishing effect under the irradiation of light sources with different brightness, so that the first defects and the second defects can be distinguished conveniently in the acquired first images and second images, and the detection accuracy is improved.
Example IV
The embodiment of the application provides a specific implementation mode of defect detection based on machine vision, which is optimized based on the embodiment, and a scheme which is not described in detail in the embodiment of the application is shown in the embodiment. The method of the embodiment of the application specifically comprises the following steps:
And adjusting the special steel sheet in the irradiation direction of the light source, moving the steel sheet until only a single pixel of the linear scanning camera falls on the gap, if the gap is smaller, the waveform can fall back, the width of the gap is smaller than that of the single pixel, if the gap is overlarge, the gap is larger than that of the single pixel, and for the optical principle, the excessive pixel brightness has a light supplementing effect on the defect, so that the width of the gap is adjusted to be equal to that of the pixel point.
The waveform of the light source brightness of the light source channel 1 in the time period of the line scanning camera T1 is adjusted, the average gray value of the acquired image is 255 under the condition of no material, and the average gray value is 10 under the condition of no material.
The waveform of the light source brightness of the light source channel 1 in the time period of the line scanning camera T2 is adjusted, and the light source controller in the time period is output to be high in brightness, so that the average gray value is 128 in the state of the diaphragm material, and the white defects such as oil stains and bright spots and the black defects such as black spots and mosquitoes can be detected.
The line scanning camera and the special light source synchronously strobe in sequence, if the defect is a pinhole, the gray value of the image defect area acquired in the T1 time period is 255, the gray value of the image defect area acquired in the T2 time period is 255, if the defect is an oil stain or a bright spot, the gray value of the image defect area acquired in the T1 time period is lower than 255 and has a larger phase difference with 255 and is about 128, and the gray value of the image defect area acquired in the T2 time period is greater than 128 and is less than or equal to 255, so that the defects such as the pinhole, the oil stain or the bright spot are distinguished.
The specific implementation manner of the defect detection based on machine vision provided by the embodiment of the application has the same beneficial effects as the embodiment.
Example five
Fig. 10 is a schematic structural diagram of a defect detection device based on machine vision according to a fifth embodiment of the present application, where the device may execute the defect detection method based on machine vision according to any embodiment of the present application, and the defect detection device includes functional modules and beneficial effects corresponding to the execution method. As shown in fig. 10, the apparatus includes:
the first image acquisition module 510 is configured to irradiate a first surface of a material in a production line with a first brightness by using a light source, and acquire an image of a second surface of the material corresponding to an irradiation area of the light source by using a line scanning camera, so as to obtain a first image; wherein the first surface is opposite the second surface;
the second image acquisition module 520 is configured to irradiate a first surface of a material in the production line with a second brightness by using a light source, and acquire an image of a second surface of the material corresponding to an irradiation area of the light source by using a line scanning camera, so as to obtain a second image; wherein the first luminance is different from the first luminance;
And a detection module 530, configured to detect a defect of the material according to the first image and the second image.
If the first brightness is smaller than the second brightness, the detection module 530 is specifically configured to:
determining a first defect of the material from the first image and determining all defects of the material from the second image;
and removing the first defect from the total defects to obtain a second defect of the material.
The detection module 530 is specifically configured to: if a pixel point range with the gray value larger than a first preset gray value exists in the first image, determining an area corresponding to the pixel point range as the first defect;
the detection module 530 is specifically configured to: if a pixel point range with the gray value larger than a second preset gray value exists in the second image, determining an area corresponding to the pixel point range as the second defect; wherein the first preset gray level is greater than the second preset gray level.
The first defect is a light transmission defect, the second defect is an opaque defect, the first preset gray level is determined according to a gray level value of white, and the second preset gray level is determined according to a gray level value of a material surface acquired by a line scanning camera when the light source irradiates with second brightness.
The first image acquisition module 510 is specifically configured to:
scanning the second surface of the material corresponding to the light source irradiation area by the line scanning camera along the first direction pixel by pixel in one scanning period of the line scanning camera; wherein the first direction is the perpendicular direction of the movement direction of the material in the production line;
scanning a second surface of the material corresponding to the light source irradiation area in a second direction by the line scanning camera in different scanning periods of the line scanning camera; wherein the second direction is the movement direction of the material in the production line.
The apparatus further comprises:
the first brightness adjustment module is used for adjusting the brightness of the light source in the process of irradiating the first surface of the material in the production line through the light source and collecting the image of the second surface of the material corresponding to the irradiation area of the light source through the line scanning camera;
and the first brightness determining module is used for taking the brightness of the light source under the condition as the first brightness if the average gray value of the collected image is a third preset gray value and the average gray value of the collected image when the material is removed is a first preset gray value.
The apparatus further comprises:
the second brightness adjustment module is used for adjusting the brightness of the light source in the process of irradiating the first surface of the material in the production line through the light source and collecting the image of the second surface of the material corresponding to the irradiation area of the light source through the line scanning camera;
and the second brightness determining module is used for taking the brightness of the light source under the condition as the second brightness if the average gray value of the acquired image is a second preset gray value.
The non-transparent plate with a gap is arranged in the irradiation direction of the light source, the gap width of the non-transparent plate is determined according to the pixel point width of the line scanning camera, and the gap area of the non-transparent plate is determined according to the scanning area of the line scanning camera in the scanning period.
The defect detection device based on machine vision provided by the embodiment of the application can execute the defect detection method based on machine vision provided by any embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method.
Example six
The embodiment of the application provides a defect detection system based on machine vision, which comprises the following steps:
the conveyor belt is used for driving the material to move;
A line scan camera for image acquisition of the surface of the material in the production line;
the light source is used for irradiating the surface opposite to the surface of the material collected by the line scanning camera;
an electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor;
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the machine vision-based defect detection method of any of the embodiments described above.
Fig. 11 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the applications described and/or claimed herein.
As shown in fig. 11, the electronic device 10 includes at least one processor 11, and a memory such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as a machine vision-based defect detection method.
In some embodiments, the machine vision-based defect detection method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the machine vision based defect detection method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the machine vision based defect detection method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable machine vision based defect detection device such that the computer programs, when executed by the processor, cause the functions/operations specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present application, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present application may be executed in parallel, sequentially, or in a different order, so long as the information desired by the technical solution of the present application can be achieved, and the present application is not limited herein.
The above embodiments do not limit the scope of the present application. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of the present application.
Claims (6)
1. A machine vision-based defect detection method, the method comprising:
illuminating a first surface of a material in a production line with first brightness through a light source, and acquiring an image of a second surface of the material corresponding to an illumination area of the light source through a line scanning camera to obtain a first image; wherein the first surface is opposite the second surface;
illuminating a first surface of a material in a production line with a second brightness through a light source, and acquiring an image of a second surface of the material corresponding to an illumination area of the light source through a line scanning camera to obtain a second image; wherein the first luminance is different from the second luminance;
Detecting defects of the material according to the first image and the second image;
if the first brightness is less than the second brightness, detecting the defect of the material according to the first image and the second image, including:
determining a first defect of the material from the first image and determining all defects of the material from the second image;
removing the first defect from the total defects to obtain a second defect of the material;
determining a first defect of the material from the first image, comprising:
if a pixel point range with the gray value larger than a first preset gray value exists in the first image, determining an area corresponding to the pixel point range as the first defect;
determining all defects of the material from the second image, comprising:
if a pixel point range with the gray value larger than the second preset gray value exists in the second image, determining the area corresponding to the pixel point range as all defects; wherein the first preset gray level is greater than the second preset gray level;
the first brightness determination process includes:
the method comprises the steps that a first surface of a material in a production line is irradiated by a light source, and the brightness of the light source is adjusted in the process of image acquisition of a second surface of the material corresponding to an irradiation area of the light source by a line scanning camera;
If the average gray value of the collected image is a third preset gray value and the average gray value of the collected image is a first preset gray value when the material is removed, taking the brightness of the light source under the condition as the first brightness;
the second brightness determination process includes:
the method comprises the steps that a first surface of a material in a production line is irradiated by a light source, and the brightness of the light source is adjusted in the process of image acquisition of a second surface of the material corresponding to an irradiation area of the light source by a line scanning camera;
if the average gray value of the acquired image is a second preset gray value, taking the brightness of the light source under the condition as the second brightness; wherein the first preset gray level is 255, the second preset gray level is 128, and the third preset gray level is 10.
2. The method of claim 1, wherein the first defect is a light-transmitting defect and the second defect is a light-opaque defect, the first preset gray level is determined according to a gray level value of white, and the second preset gray level is determined according to a gray level value of a material surface collected by a line scanning camera when the light source irradiates at a second brightness.
3. The method of claim 1, wherein image acquisition of the second surface of the material corresponding to the illuminated area of the light source by a line scan camera comprises:
Scanning the second surface of the material corresponding to the light source irradiation area by the line scanning camera along the first direction pixel by pixel in one scanning period of the line scanning camera; wherein the first direction is the perpendicular direction of the movement direction of the material in the production line;
scanning a second surface of the material corresponding to the light source irradiation area in a second direction by the line scanning camera in different scanning periods of the line scanning camera; wherein the second direction is the movement direction of the material in the production line.
4. The method according to claim 1, wherein the illumination direction of the light source is provided with a non-transparent plate with a slit, the slit width of the non-transparent plate is determined according to the pixel point width of the line scan camera, and the slit area of the non-transparent plate is determined according to the scan area of the line scan camera in the scan period.
5. A machine vision-based defect detection system, the system comprising:
the conveyor belt is used for driving the material to move;
a line scan camera for image acquisition of the surface of the material in the production line;
the light source is used for irradiating the surface opposite to the surface of the material collected by the line scanning camera;
An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor;
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the machine vision-based defect detection method of any one of claims 1-4.
6. A computer readable storage medium storing computer instructions for causing a processor to implement the machine vision-based defect detection method of any one of claims 1-4 when executed.
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Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001188048A (en) * | 1999-12-28 | 2001-07-10 | Kuramoto Seisakusho Co Ltd | Inspection method for pinhole |
| DE10203595A1 (en) * | 2002-01-30 | 2003-08-21 | Intego Gmbh | Detection of defect locations in transparent sections of glass or plastic, employs illumination source and screen with camera and computerized analysis |
| JP2009109243A (en) * | 2007-10-26 | 2009-05-21 | Panasonic Electric Works Co Ltd | Device for inspecting resin sealing material |
| KR20110001482U (en) * | 2009-08-05 | 2011-02-11 | 김교월 | The development equipment of auto inspection and sort and using method of LCD reflector Sheet |
| JP2011203201A (en) * | 2010-03-26 | 2011-10-13 | Nippon Steel Corp | Metal defect detection method |
| KR101569853B1 (en) * | 2015-06-05 | 2015-11-26 | 주식회사 넥서스원 | Apparatus and method for inspecting defect of substrate |
| KR20170018763A (en) * | 2016-05-23 | 2017-02-20 | 홍익대학교 산학협력단 | Apparatus and method for boosting a backlight based on image characteristics |
| CN110658201A (en) * | 2019-09-30 | 2020-01-07 | 苏州精濑光电有限公司 | Optical detection mechanism of diaphragm |
| CN211347985U (en) * | 2019-11-08 | 2020-08-25 | 北京大恒图像视觉有限公司 | Machine vision detection device applied to surface detection industry |
| WO2021062939A1 (en) * | 2019-09-30 | 2021-04-08 | 苏州精濑光电有限公司 | Method for optical detection of diaphragm |
| CN112763503A (en) * | 2020-12-24 | 2021-05-07 | 山西大数据产业发展有限公司 | High-grade cold-rolled silicon steel hole detection system based on machine vision |
| CN113533344A (en) * | 2021-06-30 | 2021-10-22 | 深圳中科飞测科技股份有限公司 | Optical detection device and method |
| CN113665904A (en) * | 2021-09-07 | 2021-11-19 | 钟放鸿 | Carton cigarette pack missing detection method based on TOF technology |
| CN114740011A (en) * | 2022-03-10 | 2022-07-12 | 微觉视(杭州)科技有限公司 | Defect detection device based on machine vision and defect detection method of film product |
| CN114813761A (en) * | 2022-06-27 | 2022-07-29 | 浙江双元科技股份有限公司 | Double-light-stroboscopic-based film pinhole and bright spot defect identification system and method |
| CN115272658A (en) * | 2022-05-19 | 2022-11-01 | 广州超音速自动化科技股份有限公司 | Copper foil defect detection method, system, equipment and storage medium |
| CN116532852A (en) * | 2023-06-20 | 2023-08-04 | 杭州百子尖科技股份有限公司 | Metal belt pinhole processing method, system and medium based on machine vision |
-
2023
- 2023-08-30 CN CN202311100192.9A patent/CN116818785B/en active Active
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001188048A (en) * | 1999-12-28 | 2001-07-10 | Kuramoto Seisakusho Co Ltd | Inspection method for pinhole |
| DE10203595A1 (en) * | 2002-01-30 | 2003-08-21 | Intego Gmbh | Detection of defect locations in transparent sections of glass or plastic, employs illumination source and screen with camera and computerized analysis |
| JP2009109243A (en) * | 2007-10-26 | 2009-05-21 | Panasonic Electric Works Co Ltd | Device for inspecting resin sealing material |
| KR20110001482U (en) * | 2009-08-05 | 2011-02-11 | 김교월 | The development equipment of auto inspection and sort and using method of LCD reflector Sheet |
| JP2011203201A (en) * | 2010-03-26 | 2011-10-13 | Nippon Steel Corp | Metal defect detection method |
| KR101569853B1 (en) * | 2015-06-05 | 2015-11-26 | 주식회사 넥서스원 | Apparatus and method for inspecting defect of substrate |
| KR20170018763A (en) * | 2016-05-23 | 2017-02-20 | 홍익대학교 산학협력단 | Apparatus and method for boosting a backlight based on image characteristics |
| WO2021062939A1 (en) * | 2019-09-30 | 2021-04-08 | 苏州精濑光电有限公司 | Method for optical detection of diaphragm |
| CN110658201A (en) * | 2019-09-30 | 2020-01-07 | 苏州精濑光电有限公司 | Optical detection mechanism of diaphragm |
| CN211347985U (en) * | 2019-11-08 | 2020-08-25 | 北京大恒图像视觉有限公司 | Machine vision detection device applied to surface detection industry |
| CN112763503A (en) * | 2020-12-24 | 2021-05-07 | 山西大数据产业发展有限公司 | High-grade cold-rolled silicon steel hole detection system based on machine vision |
| CN113533344A (en) * | 2021-06-30 | 2021-10-22 | 深圳中科飞测科技股份有限公司 | Optical detection device and method |
| CN113665904A (en) * | 2021-09-07 | 2021-11-19 | 钟放鸿 | Carton cigarette pack missing detection method based on TOF technology |
| CN114740011A (en) * | 2022-03-10 | 2022-07-12 | 微觉视(杭州)科技有限公司 | Defect detection device based on machine vision and defect detection method of film product |
| CN115272658A (en) * | 2022-05-19 | 2022-11-01 | 广州超音速自动化科技股份有限公司 | Copper foil defect detection method, system, equipment and storage medium |
| CN114813761A (en) * | 2022-06-27 | 2022-07-29 | 浙江双元科技股份有限公司 | Double-light-stroboscopic-based film pinhole and bright spot defect identification system and method |
| CN116532852A (en) * | 2023-06-20 | 2023-08-04 | 杭州百子尖科技股份有限公司 | Metal belt pinhole processing method, system and medium based on machine vision |
Non-Patent Citations (4)
| Title |
|---|
| Pinhole detection in steel slab images using Gabor filter and morphological features;Doo-chul Choi;《APPLIED OPTICS》;第50卷(第26期);5122-5129 * |
| 基于机器视觉的透射式薄带针孔检测系统;罗新斌;傅山;黄秀琴;邢青青;贾建昇;;有色金属加工(06);39-42 * |
| 基于机器视觉的高速宽幅铝箔针孔检测系统;廖声洋;韩震宇;董先飞;;计测技术(05);49-52 * |
| 电容屏ITO电路缺陷自动检测光照研究;刘向阳;刘志军;姜长城;全燕鸣;;机床与液压(10);41-44 * |
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