Aiming at the problems of low detection accuracy and slow detection speed in strip surface defect detection methods,a strip surface defect detection method based on improved YOLOv5 is proposed.Firstly,the Content-Aware ReAssembly of FEatures(CARAFE)is used as the upsampling operator of multi-scale feature fusion,and the Channel Scaling-Adaptively Spatial Feature Fusion(CS-ASFF)is constructed to enhance multi-scale feature fusion and control model complexity.Secondly,the GSConv and VoVGSCSP modules are introduced into the convolutional layer and cross-layer structure of the model to reduce computation and improve detection accuracy.Finally,the Focal-GIOU Loss is used as the loss function to solve the problem of imbalance between difficult and easy samples in strip defect images,thereby improving the adaptability to complex data.Experimental results show that the method achieves 80.6%mean average precision(PmAP)on NEU-DET dataset,with a calculation amount of 14.8 GFLOPs.Compared with YOLOv5,PmAP is increased by 4.3%and the computation amount is reduced by 6.33%.Compared with the current mainstream object detection networks,this method has the highest detection accuracy with a lower calculation amount and can meet the real-time detection of surface defects on steel strips in real industrial scenarios.
machine visionstrip surface defects detectionYOLOv5multi-scale fusionloss function
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