MATLAB Code Implementation for Defect Recognition

The defect recognition M-file enables comprehensive processing of defective images through median filtering and wavelet denoising techniques to enhance image quality. The implementation utilizes MATLAB's built-in functions like medfilt2() for median filtering and wavedenoise() from the Wavelet Toolbox for noise reduction. Following preprocessing, mathematical morphology operations including dilation and erosion are applied using imdilate() and imerode() functions to extract clear binary image contours. This contour extraction facilitates detailed analysis of defect shapes and dimensions, particularly for crack-like features. The code subsequently employs regionprops() function to calculate critical geometric properties such as area and perimeter measurements for identified defects. These quantitative measurements enable accurate assessment of defect severity and impact scope, providing valuable data for quality control. The integrated approach supports more effective detection and analysis of image defects, ultimately contributing to improved product quality and production efficiency through automated defect characterization.