Industrial sheet image processing technology based on machine vision

This paper designs and implements an efficient and accurate machine vision-based stator sheet image processing algorithm to address the field of precision stacking of industrial sheets. By comprehensively applying key technologies such as image processing, feature extraction, and recognition classification, the algorithm first performs preprocessing steps such as downsampling, binarization, and morphology on the original image, effectively eliminating interference information, enhancing target features, significantly improving image processing speed and efficiency, and meeting the real-time requirements of the production line. Then, a radial line search method is used to accurately locate the edge points of the sheet, enabling precise recognition and classification of stator sheets in both forward and reverse directions, ensuring high accuracy and reliability of the recognition process. The results of this study can be applied in industrial practice, driving automation and intelligence, improving production efficiency, reducing labor costs, and significantly enhancing product quality.