A hydraulic support oil cylinder barrel hole defect detection method and system based on image processing

Abstract

The present application provides a kind of hydraulic support oil cylinder cylinder bore defect detection method and system based on image processing, the present application is intercepted from the real-time monitoring video stream under the working environment of hydraulic support and contains the image frame of oil cylinder cylinder bore;High dynamic range imaging technology is applied to adjust the contrast and brightness of image frame, and second image frame is obtained;The boundary of oil cylinder cylinder bore and the internal structure change of oil cylinder cylinder bore in second image frame are identified and positioned using edge detection algorithm and threshold segmentation technology;The geometric deviation between the boundary of oil cylinder cylinder bore and the internal structure change of oil cylinder cylinder bore in second image frame and standard oil cylinder cylinder bore model is calculated, and geometric deviation result is obtained;According to geometric deviation result, the defect position of oil cylinder cylinder bore is marked in second image frame, and detection report is generated;The present application realizes the efficient and accurate detection of the oil cylinder cylinder bore of hydraulic support, improves the detection precision and efficiency, and reduces the maintenance cost.

Description

Technical Field

[0001] This invention relates to the field of industrial image processing technology, and in particular to a method and system for detecting defects in the inner bore of a hydraulic support cylinder based on image processing. Background Technology

[0002] In modern mining and engineering operations, hydraulic supports are a crucial support equipment, and their performance directly affects the safety and efficiency of the work. In particular, the cylinder components of hydraulic supports are subjected to high pressure and harsh working environments over long periods, making them prone to wear, cracks, and other defects. If these problems are not detected and addressed promptly, they will seriously affect the normal operation of the hydraulic supports and may even lead to safety accidents.

[0003] Currently, defect detection of the inner bore of hydraulic support cylinders mainly relies on manual visual inspection or traditional contact measurement methods. Manual visual inspection is affected by complex working environments and poor lighting conditions, and is also subject to subjective judgment errors, making it difficult to guarantee the consistency and reliability of the inspection results. Traditional contact measurement methods are cumbersome, time-consuming, and may cause secondary damage to the inner bore of the cylinder. Summary of the Invention

[0004] This invention provides a method and system for detecting defects in the inner bore of a hydraulic support cylinder based on image processing, in order to solve the problems of unreliable detection results, cumbersome operation, and long time consumption in the prior art.

[0005] In a first aspect, embodiments of the present invention provide a method for detecting defects in the inner bore of a hydraulic support cylinder based on image processing, comprising: Extract image frames containing the inner bore of the hydraulic cylinder barrel from the real-time monitoring video stream of the hydraulic support in its working environment; The contrast and brightness of the image frame are adjusted by applying high dynamic range imaging technology to obtain a second image frame; Edge detection algorithms and threshold segmentation techniques are used to identify and locate the boundary of the cylinder bore and the internal structural changes of the cylinder bore in the second image frame; Calculate the geometric deviation between the boundary of the cylinder bore and the internal structural changes of the cylinder bore in the second image frame and the standard cylinder bore model, and obtain the geometric deviation result, which includes the defect location information of the cylinder bore. Based on the geometric deviation results, the defect location of the cylinder bore is marked in the second image frame, and an inspection report is generated.

[0006] Optionally, extracting image frames containing the inner bore of the hydraulic cylinder from the real-time monitoring video stream of the hydraulic support's operating environment includes: A high-resolution camera mounted on a hydraulic support captures real-time video streams of the working environment inside the hydraulic cylinder barrel. Image stabilization technology is used to eliminate image jitter in the working environment video stream to obtain the target working environment video stream; The dynamic changes of the cylinder bore region in the video stream of the target working environment are detected by motion estimation algorithm in order to identify the time when the cylinder bore appears. Based on the time of occurrence, sample image frames containing multiple cylinder bores are synchronously captured from the video stream of the target working environment. Image frames containing the inner bore of the cylinder barrel are selected from the sample image frames of the multiple cylinder barrel inner bores using a target recognition algorithm.

[0007] Optionally, the step of applying high dynamic range imaging technology to adjust the contrast and brightness of the image frame to obtain a second image frame includes: The image frame is synthesized with image frames taken at different exposure levels using a high dynamic range synthesis algorithm to generate a high dynamic range image. The local contrast of the high dynamic range image is adjusted using local tone mapping technology to obtain the target high dynamic range image; A global brightness correction algorithm is used to adjust the overall brightness of the target high dynamic range image to obtain the second image frame.

[0008] Optionally, the step of using edge detection algorithms and threshold segmentation techniques to identify and locate the boundary of the cylinder bore and the internal structural changes of the cylinder bore in the second image frame includes: The second image frame is processed by an edge detection algorithm to obtain the edge information of the cylinder bore, the edge information including the boundary of the cylinder bore; Based on the edge information, the cylinder bore region in the second image frame is separated using threshold segmentation technology to obtain the segmentation result; Based on the segmentation results, a feature point detection algorithm is applied to identify key feature points in the second image frame to obtain key feature point information, which includes feature points inside the cylinder bore. Based on the key feature point information, template matching technology is used to identify and locate the boundary of the cylinder bore and the internal structural changes of the cylinder bore in the second image frame.

[0009] Optionally, the calculation of the geometric deviation between the boundary of the cylinder bore and the internal structural changes of the cylinder bore in the second image frame and the standard cylinder bore model yields a geometric deviation result. This geometric deviation result includes information on the defect location of the cylinder bore. Change information is obtained based on the boundary of the cylinder bore and the changes in the internal structure of the cylinder bore in the second image frame; Determine the standard information of the preset standard cylinder bore model, align the standard information with the variation information to obtain the aligned actual structural variation information, wherein the standard information includes size, shape and position information; The actual structural change information is input into the preset standard cylinder bore model to obtain the geometric deviation value, which includes differences in size, shape and position. The formula for calculating the geometric deviation value is as follows: ; ; in, Indicates actual structural change information Compared with the standard hydraulic cylinder bore model Geometric deviation values ​​between; This indicates the volume of the inner bore of the hydraulic cylinder. A three-dimensional function representing information about actual structural changes; It is a three-dimensional function of the standard hydraulic cylinder bore model; This represents the weighting coefficient, used to balance different types of bias. This indicates the actual structural change information in direction. Partial derivatives on; The standard model represents the direction. Partial derivatives on; This indicates the number of normal lines on the surface of the inner bore of the hydraulic cylinder. It is an index variable, representing different directions; Indicates the first Unit vectors in each direction; Parameters representing the influence of geometric deviation values; Based on the geometric deviation value, a preset defect evaluation standard is applied to determine whether there is a defect in the inner bore of the cylinder barrel, and the geometric deviation result is obtained.

[0010] Optionally, the step of marking the defect location of the cylinder bore in the second image frame based on the geometric deviation result and generating an inspection report includes: Based on the geometric deviation results, the defect location of the cylinder bore is marked in the second image frame to obtain the defect location information; Based on a preset defect classification standard, the severity of the defect at the defect location is assessed to obtain defect severity information. The defect classification standard specifies the defect category corresponding to different geometric deviations. Based on the defect location information and the defect severity information, a comprehensive analysis result is obtained; Based on the comprehensive analysis results, a detection report containing the comprehensive analysis results is generated. The detection report includes the defect location, defect description, defect level, and recommended remediation measures.

[0011] Optionally, based on a preset defect classification standard, the severity of the defect at the defect location is assessed to obtain defect severity information. The defect classification standard specifies the defect categories corresponding to different geometric deviations, including: Define the severity of the defect; The formula for calculating the severity of the defect is as follows: ; in, Indicates the severity of the defect; Represents the weight vector; It is an eigenvector; a bias term; It is the geometric feature weight vector; It is a geometric eigenvector; It is a geometric feature bias term; It is a texture feature weight vector; It is a texture feature vector; It is a texture feature bias term; It is the edge feature weight vector; It is an edge feature vector; It is the edge feature bias term; This indicates the location information of the detected defect area; Define the geometric feature vector in the severity of the defect, and introduce geometric feature expressions, which include: the area expression, the circularity expression, the depth expression, and the direction expression of the defect region; The formula for calculating the geometric feature expression is as follows: ; ; ; ; in, Indicates the area of ​​the defective region; Represents pixels in the defect area; Represents pixels The area occupied; Indicates the roundness of the defect area; It is pi; Indicates the area of ​​the defective region; Indicates the perimeter of the defective area; Indicates the depth of the defect; This indicates the number of points within the defect area where the depth was measured. Indicates the first One measurement point; Indicates the first Depth values ​​at each measurement point; The cosine similarity between the defect direction and the standard direction is represented by , where It is the direction angle of the defect. It is the standard orientation angle; This indicates the location information of the detected defect area; Define the edge feature vector in the defect severity and introduce the edge feature expression, which includes: the gradient intensity expression of the defect region, the curvature expression of the defect edge, the length expression of the defect edge, and the discontinuous defect edge expression; The calculation formula for the edge feature expression is as follows: ; ; ; ; in, Indicates the gradient intensity in the defect region; This represents the gradient of the image in the horizontal direction; This represents the gradient of the image in the vertical direction; Indicates the curvature of the defect edge; and Indicates the edge position with respect to the arc length The first derivative; and Indicates the edge position with respect to the arc length The second derivative; arc length From parameterized curves definition; Indicates the length of the defect edge; and These represent the positions of two adjacent points on the edge curve; Indicates the number of edge points; Indicates two adjacent points and The Euclidean distance between them; Indicates the edge of a discontinuous defect. and These represent the positions of two adjacent points on the edge curve; Indicates position The grayscale value at that location; This indicates the location information of the detected defect area.

[0012] Secondly, embodiments of this application provide a hydraulic support cylinder bore defect detection system based on image processing, comprising: The acquisition module is used to extract image frames containing the inner hole of the cylinder barrel from the real-time monitoring video stream of the hydraulic support in its working environment. An adjustment module is used to adjust the contrast and brightness of the image frame using high dynamic range imaging technology to obtain a second image frame; The recognition module is used to identify and locate the boundary of the cylinder bore and the internal structural changes of the cylinder bore in the second image frame using edge detection algorithms and threshold segmentation techniques. The calculation module is used to calculate the geometric deviation between the boundary of the cylinder bore and the internal structural changes of the cylinder bore in the second image frame and the standard cylinder bore model, and to obtain the geometric deviation result, which includes the defect location information of the cylinder bore. The generation module is used to mark the defect location of the cylinder bore in the second image frame based on the geometric deviation results, and generate an inspection report.

[0013] Thirdly, embodiments of the present invention provide a computing device, including a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the image processing-based hydraulic support cylinder bore defect detection method described in any of the first aspects.

[0014] Fourthly, embodiments of the present invention provide a computer storage medium storing computer program instructions thereon, wherein the computer program instructions, when executed by a processor, implement the image processing-based hydraulic support cylinder bore defect detection method described in any one of the first aspects.

[0015] In this embodiment of the invention, an image frame containing the inner bore of the hydraulic cylinder is extracted from a real-time monitoring video stream of the hydraulic support's operating environment; high dynamic range imaging technology is applied to adjust the contrast and brightness of the image frame to obtain a second image frame; edge detection algorithms and threshold segmentation techniques are used to identify and locate the boundary of the inner bore of the hydraulic cylinder and the changes in its internal structure in the second image frame; the geometric deviation between the boundary of the inner bore of the hydraulic cylinder and the changes in its internal structure in the second image frame and a standard hydraulic cylinder inner bore model is calculated to obtain a geometric deviation result, which includes the defect location information of the inner bore of the hydraulic cylinder; based on the geometric deviation result, the defect location of the inner bore of the hydraulic cylinder is marked in the second image frame, and an inspection report is generated. The technical solution provided by this invention enables efficient, accurate, and automated inspection of the inner bore of the hydraulic support cylinder, improving the accuracy and efficiency of inspection, reducing maintenance costs, and enhancing the stability of safe operation.

[0016] These or other aspects of the invention will become more apparent from the following description of the embodiments. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a method for detecting defects in the inner bore of a hydraulic support cylinder based on image processing, provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a hydraulic support cylinder bore defect detection system based on image processing, provided in an embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of a computing device provided in an embodiment of the present invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0020] In some of the processes described in the specification, claims, and accompanying drawings of this invention, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Hydraulic supports, as key components in mining and construction machinery, directly impact the reliability and safety of their cylinder bores due to the integrity of the internal bores. Traditional manual inspection methods suffer from low efficiency and insufficient accuracy, failing to meet the demands of modern industrial production. Therefore, this invention provides an image processing-based method for detecting defects in the internal bores of hydraulic support cylinders. Figure 1 ,include: Step 101: Extract image frames containing the inner bore of the cylinder barrel from the real-time monitoring video stream of the hydraulic support in its working environment; In this step, the real-time monitoring video stream refers to the continuous video stream acquired from a camera installed in the working environment of the hydraulic support; the image frame refers to a single still image extracted from the video stream.

[0023] This step involves extracting image frames containing the inner bore of the hydraulic cylinder from the real-time monitoring video stream acquired by cameras installed in the working environment of the hydraulic support. These cameras are installed to fully cover the key parts of the hydraulic support, ensuring that the video stream contains sufficient information. The portion containing the inner bore of the hydraulic cylinder is selected from the continuous video stream using video processing software and converted into a single still image for subsequent processing.

[0024] Step 102: Apply high dynamic range imaging technology to adjust the contrast and brightness of the image frame to obtain a second image frame; In this step, High Dynamic Range (HDR) imaging technology is used: an image processing technique used to improve the contrast and brightness in an image, so that details in both dark and bright areas of the image are clearly visible; This step applies high dynamic range imaging technology to process the image frame captured in step 101, adjusting the contrast and brightness of the image to improve image quality. High dynamic range imaging technology can better reveal details in the image, especially in working environments with complex or variable lighting conditions. The adjusted image is called the second image frame, which is more suitable for subsequent image processing and analysis.

[0025] Step 103: Use edge detection algorithms and threshold segmentation techniques to identify and locate the boundary of the cylinder bore and the internal structural changes of the cylinder bore in the second image frame; In this step, edge detection algorithm refers to an image processing algorithm used to identify edges in an image, namely the outline of an object or the boundary between different regions; threshold segmentation technology refers to an image segmentation technique that divides an image into different regions by setting a threshold, thereby identifying the region of interest. This step uses edge detection algorithms and threshold segmentation techniques to identify and locate the inner hole of the cylinder barrel in the second image frame. The edge detection algorithm can find the boundary of the inner hole of the cylinder barrel, while the threshold segmentation technique can separate the inner hole area of ​​the cylinder barrel from the background and further clarify the internal structural changes of the inner hole. The combination of the two techniques can more accurately identify and locate the defect location.

[0026] Step 104: Calculate the geometric deviation between the boundary of the cylinder bore and the internal structural changes of the cylinder bore in the second image frame and the standard cylinder bore model, and obtain the geometric deviation result, which includes the defect location information of the cylinder bore. In this step, geometric deviation refers to the difference between the actual object and the standard model, which usually includes differences in size, shape and position; the standard cylinder bore model refers to the ideal model of the cylinder bore in a defect-free state.

[0027] This step calculates the geometric deviation between the boundary and internal structural changes of the cylinder bore identified and located in step 103 and the standard cylinder bore model. By comparing the actual detected boundary and internal structural changes of the bore with the standard cylinder bore model, specific geometric deviation results can be obtained. The geometric deviation results include the defect location information of the cylinder bore, thereby determining the location of defects and serious defects.

[0028] Step 105: Based on the geometric deviation results, mark the defect location of the cylinder bore in the second image frame and generate an inspection report; Based on the geometric deviation results obtained in step 104, this step marks the defect location of the cylinder bore in the second image frame. The marking can be in the form of graphic annotation, color coding, etc., to visually display the specific location of the defect. A detailed inspection report is generated, which includes information such as the location and type of the defect and suggested treatment measures, so that maintenance personnel can carry out subsequent maintenance work.

[0029] The beneficial effects achieved by the embodiments of the present invention through the above steps are as follows: High dynamic range imaging technology and image processing algorithms have improved image quality and detection accuracy. It automates the entire process from extracting image frames from a video stream to generating a detection report, reducing manual intervention and improving detection efficiency; It can monitor the condition of the cylinder bore in real time in complex working environments, promptly detect and mark the location of defects, and provide a guarantee for the safe operation of the equipment.

[0030] The generated inspection report contains the specific location and type of the defect, providing a detailed basis for subsequent maintenance and repair.

[0031] In modern mining and engineering machinery, the stable operation of hydraulic supports is crucial for safe production. The condition of the cylinder bore directly affects the performance and lifespan of the hydraulic support. However, the working environment of hydraulic supports is complex, and traditional detection methods are insufficient for real-time and accurate monitoring of the cylinder bore condition. Therefore, this invention provides a specific embodiment. Step 101, extracting image frames containing the cylinder bore from the real-time monitoring video stream of the hydraulic support's working environment, specifically includes the following steps: Step 110: Capture the video stream of the working environment inside the hydraulic cylinder of the hydraulic support in real time using a high-resolution camera mounted on the hydraulic support; In this step, a high-resolution camera refers to a camera with a higher resolution, such as 1080p or higher, which can capture more details; the working environment video stream refers to the video data stream captured by the camera in real time.

[0032] This step involves mounting a high-resolution camera on the hydraulic support, ensuring that the camera covers the location of the cylinder bore. The camera captures a real-time video stream of the working environment inside the cylinder bore, ensuring that the video stream includes all dynamic information about the cylinder bore.

[0033] Step 111: Eliminate image jitter in the working environment video stream using image stabilization technology to obtain the target working environment video stream; In this step, image stabilization refers to a technique used to reduce or eliminate image jitter, typically implemented through software algorithms.

[0034] Because hydraulic supports may vibrate during operation, the image in the video stream may become jittery. Optical image stabilization (OIS) technology can be used to process the captured video stream, eliminating image jitter and resulting in a more stable video stream, i.e., the target working environment video stream.

[0035] Step 112: Use a motion estimation algorithm to detect the dynamic changes in the cylinder bore region in the target working environment video stream, so as to identify the time when the cylinder bore appears; In this step, the motion estimation algorithm refers to an image processing algorithm used to detect the motion of objects in a video sequence.

[0036] This step utilizes a motion estimation algorithm to detect dynamic changes in the cylinder bore region within the target working environment video stream. By detecting the motion of the cylinder bore region, the timing of its appearance in the video stream can be identified, thus determining when to capture a valid image frame containing the cylinder bore.

[0037] Step 113: Based on the occurrence time, simultaneously capture sample image frames containing multiple cylinder bores from the target working environment video stream; After identifying the moment when the cylinder bore appears in step 102, sample image frames containing multiple cylinder bores are synchronously captured from the target working environment video stream based on these moments. These sample image frames will be used for the next step of target recognition.

[0038] Step 114: Use a target recognition algorithm to filter out image frames containing the inner bore of the cylinder barrel in the sample image frames of the multiple cylinder barrel inner bores; In this step, the target recognition algorithm refers to an algorithm used to identify a specific target from an image.

[0039] This step uses a target recognition algorithm to filter the sample image frames captured in step 113, selecting image frames that contain the inner hole of the hydraulic cylinder barrel. The target recognition algorithm can identify the inner hole of the hydraulic cylinder barrel based on its characteristics, such as shape and texture, to ensure that the final captured image frames contain the inner hole of the hydraulic cylinder barrel.

[0040] The beneficial effects achieved by the embodiments of the present invention through the above steps are as follows: By using high-resolution cameras and image stabilization technology, the quality of the video stream is improved, image jitter is eliminated, and the accuracy of subsequent processing is ensured. By using motion estimation and target recognition algorithms, the system can intelligently identify the time of occurrence of the cylinder bore and filter out the valid image frames containing the cylinder bore, thereby improving the accuracy and efficiency of detection. The entire process is automated, reducing the need for manual intervention and improving the efficiency and reliability of testing; By monitoring the video stream in real time and identifying the moment when the cylinder bore appears, the state of the cylinder bore is monitored in real time, which helps operators to detect potential defects in a timely manner.

[0041] In the working environment of hydraulic supports, due to the complex and variable lighting conditions, image frames directly extracted from real-time monitoring video streams suffer from insufficient contrast and brightness, which directly affects the accuracy of subsequent defect detection. To overcome this problem, improve image quality, and ensure the reliability of defect detection, this invention provides a specific embodiment. Step 102, applying high dynamic range imaging technology to adjust the contrast and brightness of the image frame to obtain a second image frame, specifically includes the following steps: Step 201: Using a high dynamic range synthesis algorithm, synthesize the image frame with image frames taken at different exposure levels to generate a high dynamic range image; In this step, the high dynamic range synthesis algorithm refers to an image processing technique used to synthesize image frames taken at different exposure levels into a high dynamic range image; a high dynamic range image (HDR image) refers to an image format that has a higher dynamic range than ordinary images and can better preserve details in the dark and bright parts of the image.

[0042] This step uses a high dynamic range synthesis algorithm to synthesize the image frames captured in step 101 into a high dynamic range image, and selects the most suitable pixel values ​​in each image to retain the richest detail information, generating a high dynamic range image containing more detail.

[0043] Step 202: Adjust the local contrast of the high dynamic range image using local tone mapping technology to obtain the target high dynamic range image; In this step, local tone mapping is an image processing technique used to adjust the local contrast of an image, thereby enhancing the discernibility of details while maintaining a high dynamic range.

[0044] Although the generated high dynamic range image contains a wealth of detail, its high dynamic range may still result in overexposure or underexposure when viewed directly. Therefore, this step employs local tone mapping to adjust the local contrast of the high dynamic range image. This technique enhances the contrast of different areas within the image while maintaining the overall dynamic range, making details in both dark and bright areas more apparent, thus obtaining the target high dynamic range image.

[0045] Step 203: Adjust the overall brightness of the target high dynamic range image using a global brightness correction algorithm to obtain the second image frame; In this step, the global brightness correction algorithm refers to an image processing technique used to adjust the overall brightness of an image so that the image presents a better visual effect on the display.

[0046] This step uses a global brightness correction algorithm to adjust the overall brightness of the target high dynamic range image. The global brightness correction algorithm adjusts the overall brightness level of the image, resulting in a better visual effect on the display. By adjusting the brightness, it ensures that details in the image are clearly visible on various display devices, thus obtaining the final second image frame.

[0047] The beneficial effects achieved by the embodiments of the present invention through the above steps are as follows: By using a high dynamic range synthesis algorithm, image frames with different exposure levels are combined into a single high dynamic range image, which can better preserve details in both dark and bright areas of the image. Local tone mapping technology is used to adjust the local contrast of high dynamic range images, making the details in the image more obvious and enhancing the image's recognizability. By adjusting the overall brightness of the image through a global brightness correction algorithm, the image is ensured to be clearly visible on various display devices, thus improving the visual effect of the image. The processed second image frame has a higher dynamic range and better contrast, which helps subsequent edge detection and thresholding steps, improving the accuracy and reliability of detection.

[0048] To accurately detect defects in the inner bore of the hydraulic support cylinder, it is necessary to further extract useful key feature information from the image frames processed by high dynamic range imaging technology. Based on this, the present invention provides a specific embodiment. Step 103, which uses edge detection algorithms and threshold segmentation technology to identify and locate the boundary of the cylinder bore and the internal structural changes of the cylinder bore in the second image frame, specifically includes the following steps: Step 301: Apply an edge detection algorithm to process the second image frame to obtain the edge information of the cylinder bore, the edge information including the boundary of the cylinder bore; In this step, edge detection algorithm refers to an image processing technique used to identify the boundaries of objects in an image. Common edge detection algorithms include Canny edge detection, Sobel operator, etc.

[0049] In this step, the Canny edge detection algorithm is used to process the second image frame. The Canny edge detection algorithm detects edges by calculating the gradient of the image and applying a double thresholding technique, ultimately obtaining the edge information of the cylinder bore, which includes the boundary position of the cylinder bore.

[0050] Step 302: Based on the edge information, use threshold segmentation technology to separate the cylinder bore region in the second image frame to obtain the segmentation result; In this step, thresholding refers to an image segmentation technique that distinguishes different regions in an image by setting one or more thresholds.

[0051] After obtaining the edge information, a thresholding technique is used to separate the cylinder bore region from the background in the second image frame. For example, the OTSU thresholding algorithm can automatically select the optimal threshold to obtain a clear segmentation result. This step uses the OTSU thresholding algorithm to accurately separate the cylinder bore region from the background.

[0052] Step 303: Based on the segmentation result, apply a feature point detection algorithm to identify key feature points in the second image frame and obtain key feature point information, including feature points inside the cylinder bore. In this step, feature point detection algorithm refers to an image processing technique used to identify feature points in an image. Common algorithms include Scale Invariant Feature Transform (SIFT) and Extremely Fast Robust Feature Transform (SURF).

[0053] For example, a very fast and robust feature detection algorithm can be used to identify key feature points in the third image frame. These feature points are usually located inside the cylinder bore and can help identify structural changes in the bore. Obtaining key feature point information through feature point detection algorithms plays a crucial role in subsequent defect localization.

[0054] Step 304: Based on the key feature point information, template matching technology is used to identify and locate the boundary of the cylinder bore and the internal structural changes of the cylinder bore in the second image frame; In this step, template matching refers to an image processing technique used to identify regions in an image that are similar to a given template.

[0055] After obtaining key feature point information, this step uses template matching technology to identify and locate the boundary and internal structural changes of the cylinder bore in the second image frame. Template matching technology can identify areas similar to a standard template, accurately locate the boundary and internal structural changes of the cylinder bore, and the final second image frame contains complete boundary and structural change information.

[0056] The beneficial effects achieved by the embodiments of the present invention through the above steps are as follows: By using edge detection algorithms and threshold segmentation technology, the boundaries of the cylinder bore can be identified more accurately, thereby improving the accuracy of subsequent processing. By using feature point detection algorithms and template matching technology, structural changes inside the cylinder bore can be identified more accurately, improving the accuracy and reliability of the detection. The entire process is automated, reducing the need for manual intervention and improving the efficiency and accuracy of testing.

[0057] After identifying and locating the boundary and internal structural changes of the cylinder bore, it is necessary to further evaluate the specific details of the defects. Based on this, the present invention provides a specific embodiment. Step 104 involves calculating the geometric deviation between the boundary and internal structural changes of the cylinder bore in the second image frame and the standard cylinder bore model, obtaining the geometric deviation result. The geometric deviation result includes the defect location information of the cylinder bore, specifically including the following steps: Step 401: Based on the boundary of the cylinder bore and the internal structural changes of the cylinder bore in the second image frame, obtain change information; This step extracts information about the boundary of the cylinder bore and the changes in its internal structure from the second image frame. This information includes boundary location information obtained after edge detection and thresholding segmentation, as well as internal structure change information obtained from feature point detection. This information is then aggregated to form a description of the actual changes in the bore's state.

[0058] Step 402: Determine the standard information of the preset standard cylinder bore model, align the standard information with the change information to obtain the aligned actual structural change information, wherein the standard information includes size, shape and position information; For example, the standard hydraulic cylinder bore model is a perfect cylinder with a diameter of 100mm and a length of 500mm, with a smooth and defect-free surface. The variation information obtained in step 401 is aligned with the standard information to ensure that the differences between the two are compared in the same coordinate system. Through this alignment, the actual structural variation information after alignment is obtained, so as to calculate the geometric deviation in subsequent steps.

[0059] Step 403: Input the actual structural change information into the preset standard cylinder bore model to obtain the geometric deviation value, wherein the geometric deviation includes differences in size, shape and position; The formula for calculating the geometric deviation value is as follows: ; ; in, Indicates actual structural change information Compared with the standard hydraulic cylinder bore model Geometric deviation values ​​between; This indicates the volume of the inner bore of the hydraulic cylinder. A three-dimensional function representing information about actual structural changes; It is a three-dimensional function of the standard hydraulic cylinder bore model; This represents the weighting coefficient, used to balance different types of bias. This indicates the actual structural change information in direction. Partial derivatives on; The standard model represents the direction. Partial derivatives on; This indicates the number of normal lines on the surface of the inner bore of the hydraulic cylinder. It is an index variable, representing different directions; Indicates the first Unit vectors in each direction; Parameters representing the influence of geometric deviation values; After obtaining the actual structural changes after alignment, this information is input into a preset standard hydraulic cylinder bore model, and the geometric deviation value is obtained through a calculation formula. This calculation formula not only considers the average deviation within the volume but also incorporates the difference in partial derivatives along the surface normal direction to quantify the change in surface shape. This allows for a more comprehensive assessment of defects, and by introducing weighting coefficients, the sensitivity to different deviation types can be adjusted according to the actual situation. This geometric deviation value reflects the differences in size, shape, and position of the actual bore.

[0060] Step 404: Based on the geometric deviation value, apply the preset defect evaluation standard to determine whether there is a defect in the inner bore of the cylinder barrel, and obtain the geometric deviation result; For example, the defect assessment standard stipulates that if the geometric deviation value exceeds 5mm, it is considered that there is a defect. According to the above standard, the calculated geometric deviation value is judged. If it exceeds 5mm, it is marked as having a defect. The resulting geometric deviation value contains defect location information, such as the defect appearing at a specific location in the inner hole of the cylinder barrel.

[0061] The beneficial effects achieved by the embodiments of the present invention through the above steps are as follows: By calculating the geometric deviation between the actual structural changes and the standard model, the location and severity of defects in the cylinder bore can be identified more accurately. By using the geometric deviation calculation formula, the differences in size, shape and position of the cylinder bore are quantified, providing an objective basis for evaluation; The entire process is automated, reducing the need for manual intervention and improving the efficiency and accuracy of testing. The defect assessment standards, developed in accordance with industry norms and manufacturer requirements, ensure the scientific validity and practicality of the test results, contributing to the safe operation of the equipment.

[0062] To facilitate subsequent maintenance and repair work, the following steps will further process these deviation results. Based on this, the present invention provides a specific embodiment where step 105, according to the geometric deviation results, marks the defect location of the cylinder bore in the second image frame and generates an inspection report, specifically including the following steps: Step 501: Based on the geometric deviation results, mark the defect location of the cylinder bore in the second image frame to obtain defect location information; For example, in the second image frame, color markers, such as red rectangles or arrows, are used to mark locations where the geometric deviation value exceeds a predetermined threshold; these locations are the defect locations. After marking, an image frame containing defect location information is obtained. For example, if the geometric deviation value exceeds 5 millimeters, a marker is added near that location, and the specific location coordinates are recorded.

[0063] Step 502: Based on a preset defect classification standard, assess the severity of the defect at the defect location to obtain defect severity information. The defect classification standard specifies the defect category corresponding to different geometric deviations. For example, a predefined defect classification standard specifies the defect category corresponding to different geometric deviations. For instance, a geometric deviation between 1-3 mm is considered a minor defect, 3-5 mm a moderate defect, and greater than 5 mm a severe defect. For each marked defect location, its severity is assessed according to the predefined defect classification standard. For example, if the geometric deviation at a location is 7 mm, it is assessed as a severe defect. After the assessment, defect severity information for each defect location is obtained.

[0064] Step 503: Based on the defect location information and the defect severity information, obtain the comprehensive analysis results; This step combines the specific coordinates of each defect location with the corresponding defect severity information to form a comprehensive analysis result. For example, the comprehensive analysis result could be a table listing the coordinates of each defect location, the defect description (e.g., a dimensional deviation of 7 mm), and the defect level (e.g., "severe defect").

[0065] Step 504: Based on the comprehensive analysis results, generate a detection report containing the comprehensive analysis results. The detection report includes the defect location, defect description, defect level, and recommended remediation measures. This step generates an inspection report based on the comprehensive analysis results. The report should include the following: defect location, coordinate information of the specific defect location; defect description, describing the specific details of each defect, such as "dimensional deviation 7 mm, shape deviation 5 mm"; defect level, the severity of the defect assessed according to the defect classification standard, such as "critical defect"; and recommended repair measures, providing corresponding repair suggestions based on the defect level, for example, "It is recommended to immediately stop the machine for inspection and replace severely worn parts."

[0066] The beneficial effects achieved by the embodiments of the present invention through the above steps are as follows: By marking the defect location in the second image frame, the defect location becomes more intuitive and easier for maintenance personnel to quickly identify. Based on the preset defect classification criteria, the severity of defects is assessed to ensure the scientific nature and accuracy of the assessment results; Based on the defect location information and defect severity information, a comprehensive analysis result is generated, which facilitates a full understanding of the condition of the cylinder bore. Generates an inspection report that includes the defect location, defect description, defect level, and recommended remedial measures, providing detailed information for maintenance and repair.

[0067] To more accurately assess the defects in the inner bore of the hydraulic cylinder and provide a scientific basis for classification, a detailed analysis is needed based on a preset defect classification standard. Therefore, this invention provides a specific embodiment. Step 502 involves assessing the severity of the defect at its location based on the preset defect classification standard to obtain defect severity information. The defect classification standard specifies the defect categories corresponding to different geometric deviations, and specifically includes the following steps: Step 511: Define the severity of the defect; The formula for calculating the severity of the defect is as follows: ; in, Indicates the severity of the defect; Represents the weight vector; It is an eigenvector; a bias term; It is the geometric feature weight vector; It is a geometric eigenvector; It is a geometric feature bias term; It is a texture feature weight vector; It is a texture feature vector; It is a texture feature bias term; It is the edge feature weight vector; It is an edge feature vector; It is the edge feature bias term; This indicates the location information of the detected defect area; By introducing linear regression models from machine learning, the severity of defects can be assessed more flexibly based on different features. This approach can automatically adjust weights based on historical data, thus more accurately reflecting the actual situation.

[0068] Step 512: Define the geometric feature vector in the defect severity and introduce geometric feature expressions, which include: the area expression, circularity expression, depth expression, and direction expression of the defect region; The formula for calculating the geometric feature expression is as follows: ; ; ; ; in, Indicates the area of ​​the defective region; Represents pixels in the defect area; Represents pixels The area occupied; Indicates the roundness of the defect area; It is pi; Indicates the area of ​​the defective region; Indicates the perimeter of the defective area; Indicates the depth of the defect; This indicates the number of points within the defect area where the depth was measured. Indicates the first One measurement point; Indicates the first Depth values ​​at each measurement point; The cosine similarity between the defect direction and the standard direction is represented by , where It is the direction angle of the defect. It is the standard orientation angle; This indicates the location information of the detected defect area; By introducing these specific geometric features and assigning them their own weight vectors and bias terms, the location and nature of defects can be evaluated in greater detail, and the importance of each feature can be adjusted according to the needs of different application scenarios.

[0069] Step 513: Define the edge feature vector in the defect severity and introduce the edge feature expression, which includes: the gradient intensity expression of the defect region, the curvature expression of the defect edge, the length expression of the defect edge, and the discontinuous defect edge expression; The calculation formula for the edge feature expression is as follows: ; ; ; ; in, Indicates the gradient intensity in the defect region; This represents the gradient of the image in the horizontal direction; This represents the gradient of the image in the vertical direction; Indicates the curvature of the defect edge; and Indicates the edge position with respect to the arc length The first derivative; and Indicates the edge position with respect to the arc length The second derivative; arc length From parameterized curves definition; Indicates the length of the defect edge; and These represent the positions of two adjacent points on the edge curve; Indicates the number of edge points; Indicates two adjacent points and The Euclidean distance between them; Indicates the edge of a discontinuous defect. and These represent the positions of two adjacent points on the edge curve; Indicates position The grayscale value at that location; This indicates the location information of the detected defect area; By introducing edge features and assigning them their own weight vectors and bias terms, the edge information of defects can be evaluated more precisely.

[0070] The beneficial effects achieved by the embodiments of the present invention through the above steps are as follows: By comprehensively considering geometric features, texture features, and edge features, the assessment of defect severity becomes more comprehensive and accurate. By introducing specific characteristic expressions and calculation formulas, defect assessment becomes more scientific and operable.

[0071] Figure 2 This application provides a schematic diagram of a hydraulic support cylinder bore defect detection system based on image processing, as shown in the embodiment of the present application. Figure 2 As shown, the system includes: The acquisition module 21 is used to extract image frames containing the inner hole of the cylinder barrel from the real-time monitoring video stream of the hydraulic support working environment. The adjustment module 22 is used to adjust the contrast and brightness of the image frame using high dynamic range imaging technology to obtain a second image frame; The recognition module 23 is used to identify and locate the boundary of the cylinder bore and the internal structural changes of the cylinder bore in the second image frame using edge detection algorithms and threshold segmentation techniques. The calculation module 24 is used to calculate the geometric deviation between the boundary of the cylinder bore and the internal structural changes of the cylinder bore in the second image frame and the standard cylinder bore model, and to obtain the geometric deviation result, which includes the defect location information of the cylinder bore. The generation module 25 is used to mark the defect location of the cylinder bore in the second image frame based on the geometric deviation result, and generate an inspection report.

[0072] Figure 2 The image processing-based hydraulic support cylinder bore defect detection system described above can perform... Figure 1 The implementation principle and technical effects of the image processing-based hydraulic support cylinder bore defect detection method described in the illustrated embodiment will not be repeated here. The specific operation methods of each module and unit in the image processing-based hydraulic support cylinder bore defect detection system described in the above embodiments have been detailed in the relevant method embodiments and will not be elaborated upon here. Figure 2 The image processing-based hydraulic support cylinder bore defect detection system of the embodiment shown can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0073] The processing component 32 is used to: extract image frames containing the inner bore of the hydraulic cylinder barrel from the real-time monitoring video stream of the hydraulic support working environment; adjust the contrast and brightness of the image frames using high dynamic range imaging technology to obtain a second image frame; identify and locate the boundary of the inner bore of the hydraulic cylinder barrel and the internal structural changes of the inner bore of the hydraulic cylinder barrel in the second image frame using edge detection algorithms and threshold segmentation technology; calculate the geometric deviation between the boundary of the inner bore of the hydraulic cylinder barrel and the internal structural changes of the inner bore of the hydraulic cylinder barrel in the second image frame and the standard hydraulic cylinder barrel inner bore model to obtain a geometric deviation result, the geometric deviation result including the defect location information of the inner bore of the hydraulic cylinder barrel; mark the defect location of the inner bore of the hydraulic cylinder barrel in the second image frame according to the geometric deviation result, and generate an inspection report.

[0074] The processing component 32 includes one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component can be implemented as one or more application-specific integrated circuits (AICs), digital signal processors (DPs), digital signal processing devices (DPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0075] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (RAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0076] Computing devices also include other components such as input / output interfaces, display components, and communication components.

[0077] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices or input devices.

[0078] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0079] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0080] This invention also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The illustrated embodiment is a method for detecting defects in the inner bore of a hydraulic support cylinder based on image processing.

[0081] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0082] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0083] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting defects in the inner bore of a hydraulic support cylinder based on image processing, characterized in that, include: Extract image frames containing the inner bore of the hydraulic cylinder barrel from the real-time monitoring video stream of the hydraulic support in its working environment; The contrast and brightness of the image frame are adjusted by applying high dynamic range imaging technology to obtain a second image frame; Edge detection algorithms and threshold segmentation techniques are used to identify and locate the boundary of the cylinder bore and the internal structural changes of the cylinder bore in the second image frame; Calculate the geometric deviation between the boundary of the cylinder bore and the internal structural changes of the cylinder bore in the second image frame and the standard cylinder bore model, and obtain the geometric deviation result, which includes the defect location information of the cylinder bore. Based on the geometric deviation results, the defect location of the cylinder bore is marked in the second image frame, and an inspection report is generated; The calculation of the geometric deviation between the boundary of the cylinder bore and the internal structural changes of the cylinder bore in the second image frame and the standard cylinder bore model yields a geometric deviation result. This geometric deviation result includes information on the location of defects in the cylinder bore. Change information is obtained based on the boundary of the cylinder bore and the changes in the internal structure of the cylinder bore in the second image frame; Determine the standard information of the preset standard cylinder bore model, align the standard information with the variation information to obtain the aligned actual structural variation information, wherein the standard information includes size, shape and position information; The actual structural change information is input into the preset standard cylinder bore model to obtain the geometric deviation value, which includes differences in size, shape and position. The formula for calculating the geometric deviation value is as follows: ； ； in, Indicates actual structural change information Compared with the standard hydraulic cylinder bore model Geometric deviation values ​​between; This indicates the volume of the inner bore of the hydraulic cylinder. A three-dimensional function representing information about actual structural changes; It is a three-dimensional function of the standard hydraulic cylinder bore model; This represents the weighting coefficient, used to balance different types of bias; This indicates the actual structural change information in direction. Partial derivatives on; The standard model represents the direction. Partial derivatives on; This indicates the number of normal lines on the surface of the inner bore of the hydraulic cylinder. It is an index variable, representing different directions; Indicates the first Unit vectors in each direction; Parameters representing the influence of geometric deviation values; Based on the geometric deviation value, a preset defect evaluation standard is applied to determine whether there is a defect in the inner bore of the cylinder barrel, and the geometric deviation result is obtained.

2. The method according to claim 1, characterized in that, The step of extracting image frames containing the inner bore of the hydraulic cylinder from the real-time monitoring video stream of the hydraulic support's working environment includes: A high-resolution camera mounted on a hydraulic support captures real-time video streams of the working environment inside the hydraulic cylinder barrel. Image stabilization technology is used to eliminate image jitter in the working environment video stream to obtain the target working environment video stream; The dynamic changes of the cylinder bore region in the video stream of the target working environment are detected by motion estimation algorithm in order to identify the time when the cylinder bore appears. Based on the time of occurrence, sample image frames containing multiple cylinder bores are synchronously captured from the video stream of the target working environment. Image frames containing the inner bore of the cylinder barrel are selected from the sample image frames of the multiple cylinder barrel inner bores using a target recognition algorithm.

3. The method according to claim 1, characterized in that, The process of adjusting the contrast and brightness of the image frame using high dynamic range imaging technology to obtain a second image frame includes: The image frame is synthesized with image frames taken at different exposure levels using a high dynamic range synthesis algorithm to generate a high dynamic range image. The local contrast of the high dynamic range image is adjusted using local tone mapping technology to obtain the target high dynamic range image; A global brightness correction algorithm is used to adjust the overall brightness of the target high dynamic range image to obtain the second image frame.

4. The method according to claim 3, characterized in that, The process of identifying and locating the boundary of the cylinder bore and the internal structural changes of the cylinder bore in the second image frame using edge detection algorithms and threshold segmentation techniques includes: The second image frame is processed by an edge detection algorithm to obtain the edge information of the cylinder bore, the edge information including the boundary of the cylinder bore; Based on the edge information, the cylinder bore region in the second image frame is separated using threshold segmentation technology to obtain the segmentation result; Based on the segmentation results, a feature point detection algorithm is applied to identify key feature points in the second image frame to obtain key feature point information, which includes feature points inside the cylinder bore. Based on the key feature point information, template matching technology is used to identify and locate the boundary of the cylinder bore and the internal structural changes of the cylinder bore in the second image frame.

5. The method according to claim 1, characterized in that, The step of marking the defect location of the cylinder bore in the second image frame based on the geometric deviation result and generating an inspection report includes: Based on the geometric deviation results, the defect location of the cylinder bore is marked in the second image frame to obtain the defect location information; Based on a preset defect classification standard, the severity of the defect at the defect location is assessed to obtain defect severity information. The defect classification standard specifies the defect category corresponding to different geometric deviations. Based on the defect location information and the defect severity information, a comprehensive analysis result is obtained; Based on the comprehensive analysis results, a detection report containing the comprehensive analysis results is generated. The detection report includes the defect location, defect description, defect level, and recommended remediation measures.

6. The method according to claim 5, characterized in that, The method, based on a preset defect classification standard, assesses the severity of the defect at the defect location to obtain defect severity information. The defect classification standard specifies defect categories corresponding to different geometric deviations, including: Define the severity of the defect; The formula for calculating the severity of the defect is as follows: ； in, Indicates the severity of the defect; Represents the weight vector; It is an eigenvector; It is a bias term; It is the geometric feature weight vector; It is a geometric eigenvector; It is a geometric feature bias term; It is a texture feature weight vector; It is a texture feature vector; It is a texture feature bias term; It is the edge feature weight vector; It is an edge feature vector; It is the edge feature bias term; This indicates the location information of the detected defect area; Define the geometric feature vector in the severity of the defect, and introduce geometric feature expressions, which include: the area expression, the circularity expression, the depth expression, and the direction expression of the defect region; The formula for calculating the geometric feature expression is as follows: ； ； ； ； in, Indicates the area of ​​the defective region; Represents pixels in the defect area; Represents pixels The area occupied; Indicates the roundness of the defect area; It is pi; Indicates the area of ​​the defective region; Indicates the perimeter of the defective area; Indicates the depth of the defect; This indicates the number of points within the defect area where the depth was measured. Indicates the first One measurement point; Indicates the first Depth values ​​at each measurement point; The cosine similarity between the defect direction and the standard direction is represented by , where It is the direction angle of the defect. It is the standard orientation angle; This indicates the location information of the detected defect area; Define the edge feature vector in the defect severity and introduce the edge feature expression, which includes: the gradient intensity expression of the defect region, the curvature expression of the defect edge, the length expression of the defect edge, and the discontinuous defect edge expression; The calculation formula for the edge feature expression is as follows: ； ； ； ； in, Indicates the gradient intensity in the defect region; This represents the gradient of the image in the horizontal direction; This represents the gradient of the image in the vertical direction; Indicates the curvature of the defect edge; and Indicates the edge position with respect to the arc length The first derivative; and Indicates the edge position with respect to the arc length The second derivative; arc length From parameterized curves definition; Indicates the length of the defect edge; and These represent the positions of two adjacent points on the edge curve; Indicates the number of edge points; Indicates two adjacent points and The Euclidean distance between them; Indicates the edge of a discontinuous defect. and These represent the positions of two adjacent points on the edge curve; Indicates position The grayscale value at that location; This indicates the location information of the detected defect area.

7. A hydraulic support cylinder bore defect detection system based on image processing, characterized in that, A method for detecting defects in the inner bore of a hydraulic support cylinder based on image processing as described in any one of claims 1 to 6, comprising: The acquisition module is used to extract image frames containing the inner hole of the cylinder barrel from the real-time monitoring video stream of the hydraulic support in its working environment. An adjustment module is used to adjust the contrast and brightness of the image frame using high dynamic range imaging technology to obtain a second image frame; The recognition module is used to identify and locate the boundary of the cylinder bore and the internal structural changes of the cylinder bore in the second image frame using edge detection algorithms and threshold segmentation techniques. The calculation module is used to calculate the geometric deviation between the boundary of the cylinder bore and the internal structural changes of the cylinder bore in the second image frame and the standard cylinder bore model, and to obtain the geometric deviation result, which includes the defect location information of the cylinder bore. The generation module is used to mark the defect location of the cylinder bore in the second image frame based on the geometric deviation results, and generate an inspection report.

8. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the image processing-based hydraulic support cylinder bore defect detection method as described in any one of claims 1 to 6.

9. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements a method for detecting defects in the inner bore of a hydraulic support cylinder based on image processing, as described in any one of claims 1 to 6.

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