MATLAB-Based Crack and Scratch Detection

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In industrial production, surface defects such as cracks, scratches, and discoloration frequently pose challenges for both manual inspection and machine vision systems. These defects are difficult to detect due to their irregular shapes, low contrast variations, and interference from natural textures or patterns on product surfaces. Successful surface defect detection requires optimized lighting conditions, high-resolution cameras, precise component-camera positioning, and advanced machine vision algorithms. The scratch detection process typically involves two key steps: first, identifying the presence of scratches on the product surface, and second, extracting the scratch features using image processing techniques.

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In industrial production, various surface defects such as cracks, scratches, and discoloration frequently occur, presenting significant challenges for both manual inspection and machine vision detection systems. These defects prove particularly difficult to identify due to their irregular shapes, low contrast characteristics, and frequent interference from natural surface textures or patterns. Consequently, effective surface defect detection demands rigorous requirements including proper lighting conditions, appropriate camera resolution, precise relative positioning between inspected components and industrial cameras, as well as sophisticated machine vision algorithms.

The fundamental analysis process for machine vision scratch detection can be divided into two main stages: first, determining whether scratches exist on the product surface through image analysis techniques such as thresholding and edge detection; second, after confirming scratch presence in the analyzed images, implementing feature extraction algorithms to isolate and characterize the scratches using methods like morphological operations or contour analysis.

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