Improved YOLOv5-Based Defect Detection for Hot-Rolled Steel Strips
For the defect detection of hot-rolled strip steel,problems include too small target size,unclear fea-tures,and wrong measurement and omission detection.This paper proposes a defect detection method for hot-rolled strip steel based on improved YOLOv5.Firstly,a hyperparametric anchor frame algorithm is added to the aggregation network based on the K-means++algorithm to improve the accuracy of the anchor frame.Secondly,a new feature ex-traction module is redesigned to increase the detection scale,and a nonlinear convolution module is added to enhance the semantic information of the target defects.Finally,for the confidence loss function,a smoother relative entropy is used instead of cross-entropy to improve the stability of the model when converging.Experimental results with the benchmark algorithm show that the average detection accuracy using the improved YOLOv5 is 8.2%better than the o-riginal YOLOv5,with more vital generalization ability,faster detection,and lower error and miss detection rates.
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