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During the production of strip steel, there are often some defects on the surface of the product. Therefore, the detection of such defect is the key to produce high-quality products. At the same time, the surface defects of the steel cause huge economic losses to the high-tech industry. A steel surface defect detection algorithm based on improved YOLO-V7 is proposed to address the problems of low detection speed and poor detection accuracy of traditional steel surface defect detection methods. First, we use the de-weighted BiFPN structure to make full use of the deep, shallow and original feature information to strengthen feature fusion, reduce the loss of feature information during the convolution process, and improve the detection accuracy. Secondly, the ECA attention mechanism is combined in the backbone part to strengthen the important feature channels. Finally, the original bounding box loss function is replaced by the SIoU loss function, where the penalty term is redefined by taking the vector angle between the required regressions into account. The experimental results show that the improved model proposed in this paper has higher performance compared with other comparison models. Based on our experiments, the proposed model yields 80.2% mAP on the GC10-DET dataset and 81.9% mAP on the NEU-DET dataset with high detection speed, which is better than other existing models.

Efficient detection model of steel strip surface defects based on YOLO-V7