Surface Defect Recognition of Strip Steel Based on CNN
The surface quality of strip steel is an important indicator for measuring product performance.Accurately identifying surface defects of strip steel is a key link in the production process of strip steel.Cutting off defective strip steel is of great significance for improving the yield of strip steel.In order to improve the accuracy of surface defect recognition for strip steel,a CNN based model for surface defect recognition of strip steel was constructed.Image features were extracted through multiple convolutional layers to automatically identify defect categories,achieving end-to-end surface defect recognition of strip steel.The experimental results show that the CNN model achieves an accuracy of 96.5％for identifying surface defects on strip steel,and the recognition time for one image is only 1.5 ms,which basically meets the requirements of strip steel defect recognition.
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