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Detection and Identification of Strip Surface Defects Based on Deep Learning

As one of the important product forms in the steel industry, strip steel plays an irreplaceable role in daily production and life. However, in real life, due to the use of raw materials and the manufacturing process, there are many defects on the surface of strip steel. These defects will not only affect the appearance of strip steel, but also cause significant economic losses. Therefore, it is particularly important to accurately detect these defects on the surface of strip steel. Aiming at the problem that there are many tiny defects in strip steel, which can easily lead to missed detection, this paper improves it based on Faster R-CNN detection algorithm, uses ResNet50+FPN method to extract features to prevent missing tiny defect details, and uses ROI Align instead of ROI Pooling to perform pool operation of defect location. Because the position deviation caused by the two quantization operations of ROI Pooling can prevent the slight defect of missing detection, the experimental verification finally shows that the improved measures effectively improve the accuracy of strip surface detection compared with the previous detection algorithm.