Intelligent detection method and system for belt conveyor

Abstract

The invention relates to the technical field of image data processing, in particular to an intelligent detection method and system for a belt conveyor. The method comprises the following steps: segmenting a conveyor belt image to form a plurality of image blocks; calculating a crack score of each image block; judging the running state of the conveyor according to the crack scores of the blocks; the crack score calculation step comprises the following sub-steps: calculating the crack suspicion degree of the image block, and taking the product of the crack suspicion degree and the sample entropy of the image block as the crack degree; a crack score is determined based on the degree of crack of the tile. The method has the effect of improving the operation detection accuracy of the conveyor.

Description

Technical Field

[0001] The present application relates to the technical field of image data processing, and in particular to an intelligent detection method and system for a belt conveyor. Background Art

[0002] Belt conveyor is a common transportation equipment, which is used in mining, metallurgy, coal mining and other industries. In order to reduce the failure of conveyor during operation, it is necessary to detect the conveyor. The detection of conveyor involves the detection of multiple items, including the detection of longitudinal tearing of conveyor belt. This detection is directly related to whether the conveyor can transport materials normally, and is particularly important in the detection of conveyor. The traditional manual detection method has the problem of delayed response and low efficiency. Therefore, with the development and progress of information technology and image processing technology, the function of detecting cracks of the object to be detected by collecting images of the object to be detected has been gradually realized. The sample entropy method can be applied to the crack detection of conveyor belts; when processing image problems, the image is first divided into several blocks, and then the sample entropy value is calculated according to the gray value of the pixel points in each block, and then the sample entropy is used to judge whether there is a longitudinal crack in the object to be detected. The application of this method reduces the risk of accidents in the material transportation process to a certain extent.

[0003] However, in actual production activities, when materials move on the conveyor belt surface, they will rub against the conveyor belt surface, forming longitudinal drag marks. The drag marks will have similar grayscale values ​​to potential longitudinal cracks, and the blocks to which they belong will have highly similar sample entropy values ​​to the blocks to which potential longitudinal cracks belong. Therefore, the existence of drag marks makes the above-mentioned sample entropy-based method unable to accurately detect potential longitudinal crack defects, which in turn leads to incorrect judgment of the running status of the conveyor belt, and ultimately leads to low detection accuracy of the conveyor. Summary of the invention

[0004] In order to improve the accuracy of conveyor belt detection, the present application provides an intelligent detection method and system for a belt conveyor.

[0005] In the first aspect, the present application provides an intelligent detection method for a belt conveyor, which adopts the following technical solution: An intelligent detection method for a belt conveyor includes the steps of: segmenting a conveyor belt image to form a plurality of image blocks; calculating a crack score of each image block; judging the conveyor operation state according to the crack score of the image block; The calculation steps of the crack score include: calculating the crack suspicion of the image block, taking the product of the crack suspicion and the sample entropy of the image block as the crack degree; determining the crack score based on the crack degree of the image block; wherein the calculation formula of the crack suspicion is: ; In the formula, denote the crack suspicion degree of the th tile; denote the gray complexity within the th tile, denote the number of pixel points within the th tile whose gradient change direction is perpendicular to the longitudinal direction of the conveyor belt, denote the variance value of the gradient change directions of all pixel points with gradient changes within the th tile.

[0006] In this application, the crack suspicion degree of the tile is introduced on the basis of sample entropy, enhancing the sensitivity to cracks in the conveyor belt image. The crack suspicion degree is jointly controlled by the gray complexity of the tile, the gradient direction of the pixel points in the tile, and the gradient change of the pixel points in the tile, making the detection of cracks more accurate and sensitive. For the area with cracks, the distribution of gray levels in the tile is more chaotic and complex. Therefore, the gray complexity of the tile can be used to judge the tile with cracks. At the same time, in the tile with longitudinal cracks, there will inevitably be pixel points whose gradient direction is perpendicular to the longitudinal direction of the conveyor belt. Therefore, this factor is also an important condition for judging whether there are cracks in the tile. In addition, during the production process of the conveyor belt, in order to improve its load-bearing capacity, fibrous materials are generally embedded inside the conveyor belt. After cracks occur, the fibrous materials are exposed outside the conveyor belt, causing changes in the gradient directions of each pixel point in the conveyor belt image. Therefore, introducing the variance value of the gradient change directions of the pixel points with gradient changes in the formula can further improve the accuracy of judging whether there are cracks in the tile. Compared with the method of only judging cracks in the conveyor belt image by sample entropy in the related technology, the method in this application considers more comprehensively and has higher accuracy, thus improving the accuracy of conveyor detection.

[0007] Optionally, the calculation steps of the gray complexity of the tile include: calculating the gray diversity of the tile and the maximum gray difference of the tile; taking the product of the gray diversity and the maximum gray difference as the gray complexity of the tile.

[0008] Calculate the gray complexity of the tile collaboratively from multiple aspects, thereby improving the sensitivity to the gray level in the tile and the accuracy of subsequent crack detection.

[0009] Optionally, the calculation steps of the gray diversity of the tile include: calculating the number of different gray levels in the dataset composed of the gray values of all pixel points in the tile; calculating the total number of pixel points in the tile, and taking the ratio of the number of different gray levels to the total number of pixel points as the gray diversity of the tile.

[0010] Calculate the number of different grayscale values ​​in the tile. The larger the value, the more chaotic the grayscale of each pixel in the tile, which makes the grayscale complexity of the tile greater. Divide this number by the total number of pixels in the tile to standardize the calculation result, which is convenient for comparing the grayscale complexity of multiple tiles.

[0011] Optionally, the steps for calculating the maximum grayscale difference of a tile include: obtaining the grayscale value of the pixel with the largest grayscale value in the tile, and defining the grayscale value as the maximum grayscale value of the tile; obtaining the grayscale value of the pixel with the smallest grayscale value in the tile, and defining the grayscale value as the minimum grayscale value of the tile; setting hyperparameters, and taking the sum of the hyperparameters and the minimum grayscale value as the grayscale sum; and taking the ratio of the maximum grayscale value to the grayscale sum as the maximum grayscale difference of the tile.

[0012] The comparison between the maximum grayscale value of the pixel in the block and the minimum grayscale value reflects the degree of grayscale change in the block. The more drastic the grayscale change in the block, the greater the maximum grayscale difference of the block, and thus the greater the credibility of the grayscale value chaos in the block, thereby improving the accuracy of the grayscale complexity of the block.

[0013] Optionally, the method for segmenting the conveyor belt image comprises the steps of: selecting a segmentation reference pixel point; dividing the image into pixels of size Each area is a tile.

[0014] Optionally, the reference pixel point is a pixel point at a corner position of the conveyor belt image.

[0015] In the process of conveyor belt image segmentation, there may be pixels in the conveyor belt image that cannot be completely divided into multiple The size of the tile exists at the edge of the conveyor belt image and cannot be divided into For example, the conveyor belt image includes 60 rows and 60 columns of pixels, and the preset block size is . In the conveyor belt image segmentation process, the conveyor belt image can be divided into three blocks in the horizontal and vertical directions, and three columns of pixels and three rows of pixels remain. If the non-corner position of the conveyor belt image is selected as the reference pixel point for segmentation, there may be undivided pixels on the four edges corresponding to the conveyor belt image. Therefore, the reference pixel point is selected at the corner of the conveyor belt image, so that the two edges adjacent to the reference pixel point during the segmentation process will not have pixels that cannot be divided into blocks, which is convenient for processing the conveyor belt image.

[0016] Optionally, the crack score is a normalized value of the crack severity.

[0017] The degree of cracks is normalized to control the value range of the calculation results, which is convenient for subsequent judgment on whether there are cracks in the block.

[0018] Optionally, the method for judging the operating state of the conveyor according to the crack score of the tile includes the steps of: setting a score threshold; judging that the tile has a crack defect when the score of the tile is greater than the score threshold; and judging that the conveyor has an operating failure and issuing an alarm when there is a tile with a crack defect.

[0019] In a second aspect, the present application provides an intelligent detection system for a belt conveyor, adopting the following technical solution: An intelligent detection system for a belt conveyor, a processor and a memory, the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the intelligent detection method for a belt conveyor as described above is implemented.

[0020] Generate a computer program for the intelligent detection method of a belt conveyor as described above and store it in the memory to be loaded and executed by the processor. Thus, the system is made according to the memory and the processor, which is convenient to use.

[0021] The present application has the following technical effects: By information such as the gray complexity of the tile, the gradient of the pixel points in the tile, and the change in the gradient direction, the crack suspicion degree of the tile is calculated. The crack suspicion degree is combined with the sample entropy of the tile to calculate the crack degree of each tile, so that the detection algorithm can more accurately distinguish longitudinal cracks from drag marks and accurately identify cracks. Finally, the tiles are judged through the crack score, which improves the accuracy of the final conveyor belt detection result and further improves the accuracy of the conveyor operation result. Description of the Drawings

[0022] Figure 1 is a flowchart of an intelligent detection method for a belt conveyor according to an embodiment of the present application.

[0023] Figure 2 is a flowchart of step S2 of an embodiment of the present application. Detailed Embodiments

[0024] The embodiment of the present application discloses an intelligent detection method for a belt conveyor, which divides the image of the conveyor belt into multiple tiles, calculates a crack score that can reflect the possibility of cracks in the tiles, and judges whether there are cracks in the tiles according to the crack score.

[0025] Refer to Figure 1 , the intelligent detection method for a belt conveyor includes step S1-step S3.

[0026] Step S1: Segment the conveyor belt image to form multiple tiles; Use an image acquisition device (such as a camera) to collect the surface image of the conveyor belt of the conveyor and perform gray-scale processing on the image to obtain the conveyor belt image.

[0027] When collecting the surface image of the conveyor belt through the acquisition device, the direction of the camera should be perpendicular to the belt body to ensure that the belt body is horizontal in the surface image, which is convenient for subsequent processing of the surface image.

[0028] Determine the reference pixel point in the conveyor belt image. Taking this pixel point as the reference (which can also be understood as the starting pixel point), the conveyor belt image is divided into multiple regions of size Each region is a tile.

[0029] In this embodiment, the reference pixel point is the pixel point at the upper left corner of the conveyor belt image. In other embodiments, it can also be the pixel point at the upper right corner, lower left corner, or lower right corner of the conveyor belt image. The reason for choosing the pixel point at the corner of the conveyor belt image as the reference pixel point here is to reduce the situation where the edge of the conveyor belt image is not sufficient to form a region during the conveyor belt image segmentation process. Referring to the reference pixel point in this embodiment being the pixel point at the upper left corner of the conveyor belt image, then during the subsequent conveyor belt image segmentation process, the situation where the two edges corresponding to the reference pixel point are not sufficient to be divided into regions of size is reduced, which is convenient for subsequent calculations.

[0030] Set a width threshold. During the conveyor belt image segmentation process, if the edge of the conveyor belt image is not sufficient to be divided into regions of size When the region is small; obtain the width of the region formed by the remaining pixel points. If this width is less than the width threshold, divide the remaining pixel points into the adjacent regions that have been completed. If this width is greater than the width threshold, divide the remaining pixel points into multiple regions with a size equal to this width to form new tiles.

[0031] In this embodiment, the size of the tile is , and the width threshold is 9.

[0032] Step S2: Calculate the crack score of each tile; Referring to Figure 2 , step S2 includes steps S21 - S23 S21: Calculate the crack suspicion degree of the tile, and take the product of the crack suspicion degree and the sample entropy of the tile as the crack degree; Calculate the gray complexity of the tile; Calculate the gray diversity of the tile and the maximum gray difference of the tile; The calculation steps of the gray diversity of the tile include: calculating the number of different gray values in the dataset composed of the gray values of all pixel points in the tile; calculating the total number of pixel points in the tile, and taking the ratio of the number of different gray values to the total number of pixel points as the gray diversity of the tile.

[0033] ​The calculation steps of the maximum gray - scale difference of a tile include: obtaining the gray - scale value of the pixel point with the maximum gray - scale value in the tile and defining this gray - scale value as the maximum gray - scale value of the tile; obtaining the gray - scale value of the pixel point with the minimum gray - scale value in the tile and defining this gray - scale value as the minimum gray - scale value of the tile; setting a hyperparameter, and using the sum of the hyperparameter and the minimum gray - scale value as the gray - scale sum; using the ratio of the maximum gray - scale value to the gray - scale sum as the maximum gray - scale difference of the tile.

[0034] In one embodiment, the gray - scale complexity of a tile is the product of the gray - scale diversity and the maximum gray - scale difference.

[0035] Specifically, the calculation formula of the gray - scale complexity of a tile can be expressed as: ; where represents the gray - scale complexity within the th tile, the number of different gray - scale values in the dataset composed of the gray - scale values of all pixel points in the th tile, represents the number of pixel points within the th tile. and represent the maximum gray - scale value and the minimum gray - scale value of all pixel points within the th tile. is a hyperparameter. , and its existence is to prevent the situation where takes the value of 0.

[0036] In the formula, represents the proportion of the number of different gray - scale values in a tile to the number of all pixel points in the tile. For example, if a tile has four pixel points, and the gray - scale values of the four pixel points are 22, 23, 22, 22 respectively; it can be seen that the gray - scale values of all pixel points in the tile are divided into two categories, namely 22 and 23; then , the larger this value is, the more chaotic the gray - scale values within a block are, the greater the corresponding gray - scale chaos degree is, and the greater the possibility that the block belongs to potential longitudinal crack defects.

[0037] represents the relative size of the maximum gray - scale value to the minimum gray - scale value of the gray - scale values within each block. In the conveyor - belt image, longitudinal crack defects will have more intense gray - scale value changes. Therefore the larger it is, the greater the possibility of cracks in the tile, and the greater the corresponding gray - scale complexity.

[0038] In one embodiment, the gray - scale complexity of a tile can also be the sum of the gray - scale diversity and the maximum gray - scale difference.

[0039] Calculate the crack suspicion degree; The calculation formula for the crack suspicion degree is as follows: ; In the formula, represents the crack suspicion degree of the th tile; represents the gray complexity within the th tile, represents the number of pixel points whose gradient change direction is perpendicular to the longitudinal direction of the conveyor belt within the th tile, represents the variance value of the gradient change directions of all pixel points with gradient changes within the th tile.

[0040] In the formula, for the tiles where there is no gradient change, the corresponding and do not exist. In this case, let and both be 1.

[0041] The crack suspicion degree is used to represent the possibility of cracks in the tile. In the formula, represents the number of pixel points whose gradient direction is perpendicular to the longitudinal direction of the conveyor belt in the tile. In a tile, if is larger, it indicates that the texture in the tile is more likely to have the characteristic of longitudinal extension, and thus the corresponding crack suspicion degree is larger.

[0042] During the production and manufacturing process of the conveyor belt, in order to improve the load-bearing capacity of the conveyor belt itself, fiber materials are usually embedded inside. When cracks appear in the conveyor belt, these fibers will be exposed outside the conveyor belt, forming unique characteristics on the surface of the conveyor belt. Therefore, this feature can be used to further judge the crack suspicion degree of the tile. In the formula, represents the variance value of the gradient direction change value. The larger this value is, it indicates that the number of pixel points with inconsistent gradient change directions in the tile is more, which further indicates that the possibility of different-direction textures existing in the tile is greater (corresponding to the fibrous substances exposed outside the conveyor belt), and ultimately makes the crack suspicion degree of the tile larger.

[0043] S22: Calculate the crack degree; The crack degree is the product of the crack suspicion degree and the sample entropy of the tile.

[0044] The calculation formula for the crack degree can be expressed as: ; In the formula, represents the crack degree of the th sub-block; represents the crack suspicion degree of the th tile; represents the The sample entropy of the tile. The calculation of the sample entropy is common knowledge to those skilled in the art and will not be elaborated here.

[0045] S23: Determine the crack score based on the crack degree of the tile; The crack score of the tile is the value after normalizing the crack degree of the tile. In this embodiment, the function is used to normalize the crack degree.

[0046] Step S3: Judge the running state of the conveyor according to the crack score of the tile; Set a score threshold; when the score of the tile is greater than the score threshold, it is judged that the tile has a crack defect; when there is a tile with a crack defect, it is determined that the conveyor has a running fault and an alarm is issued.

[0047] The embodiment of the present application also discloses an intelligent detection system for a belt conveyor, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, an intelligent detection method for a belt conveyor according to the present application is implemented.

[0048] The above system also includes other components well-known to those skilled in the art such as a communication bus and a communication interface. Their settings and functions are known in the art and will not be elaborated here.

[0049] The above are all preferred embodiments of the present application. The protection scope of the present application is not limited by this. Therefore, all equivalent changes made according to the structure, shape, and principle of the present application should be covered within the protection scope of the present application.

Claims

1. An intelligent detection method for a belt conveyor, characterized in that: The method comprises the steps of: segmenting the conveyor belt image to form a plurality of image blocks; Calculate the crack score for each tile; Determine the conveyor operation status based on the crack score of the block; The calculation steps of the crack score include: calculating the crack suspicion of the image block, taking the product of the crack suspicion and the sample entropy of the image block as the crack degree; determining the crack score based on the crack degree of the image block; wherein the calculation formula of the crack suspicion is: ; In the formula, Indicates The crack suspicion of each tile; Indicates The grayscale complexity within a block, Indicates The number of pixels in a block whose gradient change direction is perpendicular to the longitudinal direction of the conveyor belt, Indicates The variance value of the gradient change direction of all pixels with gradient changes in a block.

2. The intelligent detection method for a belt conveyor according to claim 1 is characterized in that: The steps of calculating the grayscale complexity of the block include: calculating the grayscale diversity of the block and the maximum grayscale difference of the block; and taking the product of the grayscale diversity and the maximum grayscale difference as the grayscale complexity of the block.

3. The intelligent detection method for a belt conveyor according to claim 2 is characterized in that: The calculation steps of the grayscale diversity of the block include: calculating the number of different grayscale values ​​in a data set composed of the grayscale values ​​of all pixels in the block; calculating the total number of pixels in the block, and taking the ratio of the number of different grayscale values ​​to the total number of pixels as the grayscale diversity of the block.

4. The intelligent detection method for a belt conveyor according to claim 2 is characterized in that: The steps for calculating the maximum grayscale difference of a block include: obtaining the grayscale value of the pixel with the largest grayscale value in the block, and defining the grayscale value as the maximum grayscale value of the block; obtaining the grayscale value of the pixel with the smallest grayscale value in the block, and defining the grayscale value as the minimum grayscale value of the block; setting a hyperparameter, and taking the sum of the hyperparameter and the minimum grayscale value as the grayscale sum; and taking the ratio of the maximum grayscale value to the grayscale sum as the maximum grayscale difference of the block.

5. The intelligent detection method for a belt conveyor according to claim 1, characterized in that: The method for segmenting a conveyor belt image comprises the steps of: selecting a segmentation reference pixel point; dividing the image into Each area is a tile.

6. The intelligent detection method for a belt conveyor according to claim 5, characterized in that: The reference pixel points are the pixel points at the corner positions of the conveyor belt image.

7. The intelligent detection method for a belt conveyor according to claim 5, characterized in that: In response to the remaining pixels at the edge of the conveyor belt image being insufficient to be divided into When a block of a certain size is used, the width of the area formed by the remaining pixels is calculated; in response to the width being less than a preset width threshold, the remaining pixels are divided into adjacent areas that have already been divided; in response to the width being greater than the preset threshold, the remaining pixels are divided into new areas.

8. The intelligent detection method for a belt conveyor according to claim 1, characterized in that: The crack score is the normalized value of the crack severity.

9. The intelligent detection method for a belt conveyor according to claim 1, characterized in that: The method for judging the running state of a conveyor according to the crack score of a block comprises the steps of: setting a score threshold; judging that a crack defect exists in the block in response to the score of the block being greater than the score threshold; judging that a conveyor operation failure exists and issuing an alarm in response to the presence of a block with a crack defect.

10. An intelligent detection system for a belt conveyor, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an intelligent detection method for a belt conveyor according to any one of claims 1 to 9 is implemented.

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