Surface Defect Detection of Steel Plate Based on KAS-YOLO
As the difficulty in identifying small defect targets,the vision-based of steel plate surface defect detection method has the problem of low detection accuracy.The model KAS-YOLO based on YOLOv5s is proposed for steel plate surface defect detection.Firstly,the hollow space pyramid pooling module is used to obtain a larger receptive field to extract more feature information of surface defects,and coordinate attention mechanism is chosen to improve the feature extraction ability.Then the K-means algorithm is used to obtain a more matched anchor frame,which not only increases the number of positive samples,but also accelerates the convergence of the model.Finally,the loss function of SIoU is used to improve the ability of locating and detecting for surface defect targets furtherly.The experimental results show that the proposed KAS-YO-LO model is superior to the methods such as Faster R-CNN,SSD,RetinaNet and YOLOv5s in the detection accuracy and speed for the detection of steel plate surface defects.
surface defect of steel sheetYOLOv5sattention mechanismanchorloss function
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