In order to meet the requirements of rapid and accurate online detection of surface defects on cold-rolled steel strips,an efficient new four neighborhood difference defect segmentation algorithm has been developed.This algorithm uses the average grayscale of image blocks as local features and achieves fast calculation by integrating the image.It simultaneously introduces high and low threshold values to divide the grayscale difference values to improve the algorithm's anti-interference ability.The experimental results show that compared with traditional segmentation methods such as SIFT and SURF,this algorithm significantly reduces computation time while maintaining high accuracy.In the testing of various typical cold-rolled strip surface defect samples,the algorithm demonstrated excellent segmentation performance,with an average accuracy of over 95％and an average processing time of no more than 50 ms for 128 × 128 pixel images.It proves that the algorithm can effectively balance detection accuracy and speed,providing a practical solution for online detection of surface defects in cold-rolled steel strips.It is expected to play an important role in practical production.
维普
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