Aiming at the problems of large number of parameters and high computational complexity of current main-stream defect detection models,it is difficult to deploy on embedded devices with limited computing resources.A lightweight steel surface defect detection model YOLO-LSNet is proposed.Firstly,in order to reduce the parameter number and compu-tational complexity of the model,a lightweight convolution module MSConv is proposed.Secondly,M-BiFPN network is proposed for deep and shallow layer feature information fusion.Finally,CIoU loss function is replaced by SIoU loss function to speed up the convergence of the network.The experimental results show that compared with the baseline network YOLOv5 in NEU-DET data set,the mAP of YOLO-LSNet model increases by 1.8％,model parameters decreases by 43.4％,and computation cost decreases by 36.1％.At the same time,the lightweight design of the model is completed,the detection accuracy of the model is guaranteed,and it has a good application prospect.
维普
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