JPH0843316A - Automatic diagnosing method for coating film deterioration and its device - Google Patents

Abstract

PURPOSE:To provide an automatic diagnosing method for coating film deterioration and its device which can conduct automatic setting of calibration and a process range in evaluation of a deterioration part of painting surface based on image information taken into a computer. CONSTITUTION:The device, in which a painting surface 2 applied to a structure such as a steel bridge is taken into an image input device 3 and a deterioration part of the painting surface 2 is diagnosed based on the image, is provided with an object structure data base 9 wherein dimensions of characteristic parts of a structure to be inspected are stored; an image processing device 4 which takes in an image from the image input device 3 and detects characteristic parts in the image; and a calculating-diagnosing device 6 into which image data of the image processing device 4 as well as data of the object structure data base 9 are inputted and which conducts calibration based on both the data, determines a process range of the painting surface 2, and diagnoses deterioration of coating film.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a coating film deterioration automatic diagnosing method and apparatus for detecting the degree of deterioration of a painted surface of a structure such as a steel bridge.

[0002]

2. Description of the Related Art A steel material constituting a structure such as a steel bridge is coated for rust prevention, but the coated surface is deteriorated with the lapse of years, and the coating film is cracked or peeled. Since rust and the like occur, the coating film surface is regularly inspected for deterioration, and maintenance such as repainting is performed based on the inspection.

Conventionally, in the inspection of the coated surface, the degree of deterioration has been judged by visually observing the coated surface to be inspected. However, the person in charge cannot eliminate the individual difference, resulting in lack of objectivity. There is.

Therefore, an attempt has been proposed to accumulate a visual evaluation result as a database and develop a coating film deterioration diagnosis system.

This coating film deterioration diagnosis system is disclosed in Japanese Patent Application No. 4-
No. 271847 (Title of Invention: Method and apparatus for diagnosing coating film deterioration), the coated surface to be inspected is CC
The image is taken by a D camera or the like, this image information is taken into a computer, the deteriorated portion is extracted from the image, and the deteriorated portion is diagnosed on the basis of a database created based on a predetermined criterion. is there. In particular, the captured image is affected by the shading that occurs on the shooting and the structure of the painted surface, such as the contrast of the painted surface caused by light and shade of light, such as shadows. The morphology process is applied to remove the effect of seeding, and the gray-scale change area that is caused by cracks, peeling, rust, etc. is extracted as an individual deformed portion, and the area of the individual deformed portion (deteriorated portion) is extracted purely from the painted surface. The difference between the average gray value and the gray value is calculated, and the degree of deterioration of the coated surface is diagnosed by comparing with the above database.

In the invention of this prior application, when diagnosing the coated surface, the actual dimension of the deteriorated portion is obtained, but in order to obtain the actual dimension from the image, the operator needs to perform calibration setting of the image. . Therefore, when photographing the painted surface, an operator attached a scale to a part of the painted surface and the operator
The actual size calibration is set by looking at the scale on the image.

[0007]

However, there are many painted surfaces to be inspected, and if the scale is attached to each painted surface, there is a problem that the efficiency becomes poor. It is also possible for the operator to perform the calibration operation by looking at the characteristic part of the image without using a scale, but if it is operated for each image, the operation is difficult and there are individual differences. However, there are some problems that cannot be avoided.

Further, in the prior application, in order to set the processing area for the deterioration diagnosis of the painted surface from the image, the operator must manually set the processing range, which is easy to burden the operator. There was a problem.

Therefore, an object of the present invention is to solve the above-mentioned problems and to automatically set calibration and to automatically set a processing range when evaluating a deteriorated portion of a painted surface from image information taken in a computer. An object is to provide a coating film deterioration automatic diagnosis method and apparatus that can be set.

[0010]

In order to achieve the above-mentioned object, the invention of claim 1 takes in a coated surface applied to a structure such as a steel bridge into a computer through an image input device,
In the method of diagnosing the deteriorated part of the painted surface, the dimensions of the characteristic part of the structure to be inspected are stored as data, the characteristic part is detected from the captured image, and calibration is performed based on the above data. Then, the coating film deterioration automatic diagnosis method is characterized in that the processing range of the coating surface to be inspected is set and the deterioration of the coating film is diagnosed.

According to a second aspect of the present invention, in a device for inspecting a deteriorated portion of the painted surface from the image by taking in the painted surface applied to the structure such as a steel bridge into an image input device, the feature of the structure to be inspected Target structure database in which the dimensions of specific parts are stored, an image processing device that captures an image from an image input device and detects a characteristic part in the image, and image data of the image processing device is input and the target Data of the structure database is input, calibration based on both data is set, and a processing / diagnosis device for diagnosing coating film deterioration by setting the processing range of the painted surface to be inspected is provided. It is a coating film deterioration automatic diagnosis device that does.

[0012]

According to the above construction, the shape of the painted surface of the structure to be photographed is, for example, the lower surface of the flange of the I-shaped steel material,
The width dimension of the bottom surface of the steel material is stored in the target structure database as the dimension of the characteristic portion, and the dimension of the characteristic portion is obtained by detecting the edge of the flange portion by image processing from the captured image. From the above dimensions and the actual dimensions from the database, calibration is performed to convert how many mm the number of pixels corresponding to the above-mentioned dimensions in the actual dimensions will be. Except for this, deterioration diagnosis can be performed automatically by setting the processing range to be inspected.

[0013]

An embodiment of the present invention will be described in detail below with reference to the accompanying drawings.

The main configuration of this system will be described with reference to FIG.

First, reference numeral 1 denotes a painted surface image including a painted surface 2 of a structure, and the painted surface image 1 is taken into an image input device 3. The image input device 3 may be of any type as long as it can be input to the image processing device 4 of the computer. For example, a CCD camera for directly inputting an image to the image processing device 4 or a VTR tape on which a painted surface image is photographed is used. Even in the format in which the image reproduced from the video deck is input to the image processing apparatus 4, the photograph of the painted surface image 1 is read by a scanner or the like and input to the image processing apparatus 4, or the negative of the photograph is stored in the photo CD. Photo CD to CD-ROM
The format may be one in which the image is reproduced by a drive and the image signal is input to the image processing apparatus 4, or one in which the image signal is captured by the image processing apparatus 4 by personal computer communication.

The image information from the image input device 3 is taken into the image processing device 4 of the computer, and the image processing device 4
Thus, various image processings are performed to extract a deteriorated portion, detect an edge 2a which is a characteristic portion of the painted surface 2, and detect a width (dimension) h of the detected edge 2a. The image captured by the image processing device 4 is sent to the image frame memory 5 and the calculation / diagnosis device 6.

The image frame memory 5 includes an image processing device 4
Is connected to the calculation / diagnosis device 6, and manages a plurality of images captured by the computer, and calculates the current image on the image processing device 4 side, the result image after image processing, etc.
The diagnostic device 6 can also refer to it.

A keyboard 7 and a display device 8 are connected to the arithmetic / diagnosis device 6. The arithmetic / diagnosis device 6 can access the target structure database 9, the inspection history database 10, and the judgment reference database 11, and the actual size calibration process, the setting of the processing range, and the geometry of the deteriorated portion are performed from the image from the image processing device 4. The evaluation process of the biological characteristic amount and the deterioration degree is performed, and these inspection results are displayed on the display device 8 in the form of a table, a graph, or an image. Further, the data of the processing results are the inspection history database 10 and the judgment standard database 1
It is stored in 1 and stored as a database of subsequent examinations.

The target structure database 9 records the dimensions of the characteristic portions of the steel material on the painted surface of the structure to be inspected, for example, the dimensions of the flange portion of the I-shaped steel material and the installation environment. The diagnostic device 6 uses the information from the database 9 and the dimension data of the characteristic portion from the image processing device 4 to perform actual dimension calibration and setting of a processing range, and also to extract a deteriorated portion and evaluate the degree of deterioration. To do.

The criteria database 11 stores criteria for evaluating the degree of deterioration based on the geometrical feature of the deteriorated portion, and is used in the evaluation of the degree of deterioration.

The inspection history database 10 records the current image used in the inspection, the processed image, the inspection result, and the like, and the past inspection result can be referred to during the diagnosis by the arithmetic / diagnosis device 6. ing.

Next, an outline of the processing flows 20 to 26 shown in FIG. 2 of the diagnostic method of the present invention will be described.

Image input 20: Image information of the painted surface is input to the system through the image input device 3 from a photograph of the painted surface taken on site, a video tape, or directly from the site. If there are multiple pieces of image information to be input, input them all at once. In FIG. 1, the image is input to the image processing apparatus 4, but a plurality of images may be input via the image frame memory 5 and the images may be sequentially sent to the image processing apparatus 4. .

The input image is transferred to the image frame memory 5 after image processing.

Actual dimension calculation 21 (actual dimension calibration): The calibration operation is performed by setting the positions of the characteristic portions on the image of two points of known dimensions and the actual dimension between the two points.

As the two known dimensions of the characteristic portion, the edges of both ends of the steel material which is the painted surface, or the dimension of the known characteristic portion appearing on the image (for example, the scale dimension of the scale) is used. Good.

The positions of the edges at both ends and the distance (width dimension) on the image between the edges are obtained by using an image processing technique,
The actual dimensions of both edge portions are obtained from the target structure database 9.

That is, in FIG. 1, how many millimeters the actual dimension is with respect to the number of pixels corresponding to the dimension h between the edges 2a, for example, the number of pixels corresponding to the dimension h is 400 and the actual dimension is 300.
If it is mm, the number of one pixel is about 1.33 mm, and the calibration is performed based on this converted value.

Setting of processing range 22 By using the image processing technique from the image data of the coated surface, both edge portions of the steel material are obtained, and the processing area is automatically set inside the steel material. Finally, a mask image that makes only the processing range effective is created and transferred to the image frame memory 5.

In setting the processing range, when performing the density morphology processing, the filter size (16 × 16 dot mass, in the above example, the actual size is about 2) with respect to the processing range.
(1 × 21 mm square), the average density value of each area is obtained. Therefore, the row and column widths of the processing range on the image are each an integral multiple of the filter size, and the processing range is
Set them so that they are all inside the edge and inside the left and right width of the image.

As a method for extracting the edge portion of the steel material, differential filtering processing (sobel filter or the like) binarization processing is sequentially performed on the image data of the coated surface to obtain a portion where the gray value suddenly changes.

Two long straight line portions are obtained by performing a straight line detection process such as the least squares method and the Hough transform.

Processing such as the above is performed.

The processing range is set to be between the edge portions at both ends of the steel material (slightly inside) with the exception of the peripheral portion of the image having a predetermined width.

If there is an unnecessary portion such as an artificial label inside the target coated surface, the operator sequentially specifies the vertices of the outer peripheral polygon of the unnecessary portion by operating the mouse or the cursor keys. It can also be dealt with manually.

Deterioration processing 23: The image processing device 4 performs image processing on the inside of the processing range of the painted surface image based on the input current image of the painted surface and the processing range mask image on the image frame memory 5. By using the technique, the deteriorated portion is extracted.

As an example of the method of extracting the deteriorated portion, since the deteriorated portion is extracted without being affected by the irradiation unevenness such as the shadow of the steel material, etc., the dark and light morphology in which only the dark portion is locally extracted from the surroundings is extracted. Application of treatment etc. is considered.

The deteriorated portion image after processing is transferred to the image frame memory.

Characteristic amount calculation 24: Various geometrical characteristic amounts necessary for the deterioration degree evaluation and their statistical amounts are calculated based on the deteriorated portion image. The basic procedure is as follows.

Labeling processing is performed on the deteriorated portion image.

Geometrical feature quantities such as area, perimeter, and circularity are calculated for each label.

With respect to the above-mentioned various feature amounts, the maximum value, the minimum value, the average value, the statistic amount such as the variance, and the number of deteriorated parts are obtained.

The area occupancy of the deteriorated portion is obtained from the area of the entire processing region and the area of the entire deteriorated portion.

The particle size is calculated for each label, and the area occupancy of the deteriorated portion is calculated for each particle size.

Evaluation of Degradation Degree in Image Unit 25: Degradation degree of one image is evaluated by comparing the contents of the judgment reference database with the obtained various feature amounts. As an example of the evaluation criterion of the deterioration degree, the area occupancy rate of the deteriorated part in the entire image and the deteriorated part are classified according to the particle size, and the deterioration rate is evaluated based on the area occupancy rate of each group.

Comprehensive evaluation 26 of deterioration degree: First, evaluation is made in units of steel material, and then evaluation is made of the entire structure.

The evaluation for each steel material is calculated by statistically processing the judgment results of a plurality of images contained in the same steel material. At this time, with respect to the image in which the determination result has a large deviation from the average value, it is determined that the processing such as extraction of the deteriorated portion is not properly performed, and the image is excluded from the evaluation of the steel material. If necessary, the operator can judge the quality of the image.

The evaluation of the entire structure is calculated by obtaining a weighting factor representing the degree of importance of each steel material from the target structure database 9 and statistically processing the evaluation result of each member such as a weighted average. As a result, the degree of deterioration of the entire structure can be evaluated without deviation and the evaluation of each steel material can be performed, so that it is possible to judge partial repainting.

Other processing: The image information of the inspected coated surface and the inspection information are displayed on the display device 8 as necessary and are stored in the inspection history database 10. Further, the past inspection result can be referred to from the inspection history database 10 and the reinspection can be performed.

Next, the flow chart of the processing flow shown in FIG. 2 will be described with reference to FIG.

In FIG. 3A, when the diagnosis is started 30, a photograph 31 of the coated surface is taken. In the present invention, the edge dimension is obtained and calibrated for each image, but it is possible to photograph under the same conditions with a photographing magnification of one or several diameters of the steel bridge by automatic photographing of the painted surface by a robot or the like,
In this case, in order to save the labor of calibration later,
Take pictures under the same conditions as much as possible.

This calibration algorithm will be described.

As a photographed image, a large image is used for a panoramic reference image showing the installation environment of a steel bridge. An image for calculating actual dimensions including a ruler member of a known dimension. Take 3 types.

The image input procedure is basically such that the image for actual size calculation is first loaded into the system and then the scale conditions (focal length of lens, distance between painting surface and camera, etc.) are set to the same conditions. The captured deterioration evaluation image is captured.

That is, all the deterioration degree evaluation images captured after the actual size calculation image are imaged so as to have the same dimensions as the actual size calculation image, and when the focal length of the camera lens is changed. Before the deterioration degree evaluation image is captured, the actual dimension calculation image is calculated with the corresponding dimension ratio so that the appropriate dimension ratio can be calculated.

However, since the full view reference image is a reference image, it is not an object of evaluation, but the whole inspection range is set from this full view reference image, and the above-mentioned actual size calculation is made in the target structure database. It is used when storing the input order data of an image and the image for deterioration degree evaluation accompanying it.

The flow chart of FIG. 3 will now be described. As described above, the actual size calculation image and the deterioration degree evaluation image image associated therewith are input 20, and in step 1, it is determined that the input entire image inspection is completed. If the inspection of all the images is not completed (no), the process moves to step 2 of the subroutine shown in FIG. 3B, and it is determined whether the image is an actual size calculation image. The first input image is a real image as described above (yes), and then the real size calibration 21 is performed.

This calibration 21 will be described with reference to FIG. 5. The actual size calculation image 35 includes the painted surface 2, the background portion 36 indicated by diagonal lines, and the portion 37 which is painted but is unnecessary for inspection. Composed and its painted surface 2
The dimension h of the edge portions 2a, 2a of the is known from the photograph or the type of the steel material photographed, and the actual dimension data is stored in the target structure database 9 in advance and read at the time of the inspection. The dimension data 38 is obtained. On the other hand, from the actual size calculation image 35, the position of the edge portion 2a is detected by the image processing device 4 and the size h on the screen is obtained. The actual dimension conversion value 39 is obtained from the dimension h on the image and the actual dimension data 38, and the calibration is completed.

Next, as shown in FIG. 3B, the processing range is set 22, and then the deteriorated portion extraction 23, the characteristic amount calculation 24, and the image deterioration degree evaluation 25 are performed, and the process exits the subroutine and returns to step 1. Be done. After that, the next deterioration degree evaluation image is inspected. At this time, in the determination of step 2,
Since it is not an actual image (no), the processing range setting 22 is made without performing the calibration 21.

This processing range setting 22, as shown in FIG. 6 (a), the image 40 is substantially the same as the actual size calculation image 35 described in FIG. 5, and as shown in FIG. 6 (b). The edge portion 42a analyzed by the image processing of the dimension calculation image 35 is located at the same position, and the shaded area inside the edge portion 42a is set as the processing setting range 22, and other background portions 36 and unnecessary portions 37 are , Removed and stored in the image frame memory 5, and as shown in FIG.
To image deterioration degree evaluation 25 are performed. Similarly, without performing the calibration 21 of the next deterioration degree evaluation image, the image deterioration degree evaluation 2 is performed from the processing range setting 22.
Processing up to 5 is performed.

In this way, the image deterioration degree evaluation 25 of the deterioration degree evaluation image is sequentially performed. However, if it is determined that the image is an actual image again in step 2 (yes), the calibration 21 is performed again, and then the calibration is performed. Then, the image for deterioration degree evaluation is inspected based on section 21.

It is needless to say that the determination of step 2 is unnecessary when the calibration is performed on all the processed images.

FIG. 4 shows five images 45-1 to 45-5 to be inspected based on the flow chart of FIG. 3, and the images 45-1 and 4-3 are actually calibrated. The image 45-2 is a dimension calculation image, and the image 45-2 and the image 45-4 are images for deterioration degree evaluation on the same scale as the image 45-1 that was initially calibrated.
-5 is an image 45-3 that has been calibrated next
The image for deterioration degree evaluation of the same scale as is shown. In this manner, by inspecting the actual size calculation image and the deterioration degree evaluation image in order of image order, efficient inspection diagnosis can be performed without setting calibration for each sheet.

Next, if the entire image inspection is completed in step 1 of FIG. 3A (yes), the comprehensive evaluation 2 of the deterioration degree is given.
6. The result is displayed and saved 27, and the diagnosis is finished 32.

[0065]

In summary, according to the present invention, the dimensions of the characteristic portion of the painted surface are obtained from the captured image by image processing, and the calibration is performed from the dimensions on this image and the actual dimensions from the database. The deterioration diagnosis is automatically performed by setting the processing range to be inspected by excluding the portion unnecessary for inspection from the screen.

[Brief description of drawings]

FIG. 1 is a system configuration diagram showing an embodiment of the present invention.

FIG. 2 is a schematic diagram of a processing flow of the present invention.

FIG. 3 is a diagram showing a flowchart of the present invention.

FIG. 4 is a diagram illustrating a relationship between a real size image for calibrating an image to be diagnosed and a deterioration degree evaluation image in the present invention.

FIG. 5 is an explanatory diagram when performing calibration setting from an image in the present invention.

FIG. 6 is an explanatory diagram for setting a processing range from an image in the present invention.

[Explanation of symbols]

 2 Painted surface 3 Image input device 4 Image processing device 6 Calculation / diagnosis device 9 Target structure database

Front page continuation (72) Inventor Yukihiro Kono 3-15-1, Toyosu, Koto-ku, Tokyo Ishikawajima Harima Heavy Industries Ltd. Toni Technical Center (72) Inventor Takaaki Akai 3-1-1, Toyosu, Koto-ku, Tokyo No. 15 Ishikawajima Harima Heavy Industries Co., Ltd. Toni Technical Center (72) Inventor Hiroshi Fujiwara 1-19-5 Shimorenjaku, Mitaka City, Tokyo

Claims (2)

[Claims] 1. A characteristic part of a structure to be inspected in a method of diagnosing a deteriorated part of a painted surface by loading a painted surface applied to a structure such as a steel bridge into a computer through an image input device. The size of the data is stored as data, the characteristic part is detected from the captured image, and the calibration is performed based on the above data.After that, the processing range of the painted surface to be inspected is set to prevent the deterioration of the coating film. A method for automatically diagnosing coating film deterioration, which comprises diagnosing. 2. A dimension of a characteristic portion of a structure to be inspected in a device for recognizing a deteriorated portion of a painted surface from an image by inputting a painted surface applied to a structure such as a steel bridge to an image input device. The target structure database in which is stored, the image processing device that captures the image from the image input device and detects the characteristic portion in the image, and the image data of the image processing device is input and the data of the target structure database Is input, calibration is performed based on both data, and the coating / processing device is equipped with a calculator / diagnostic device for diagnosing coating film deterioration by setting the processing range of the coating surface to be inspected. Diagnostic device.