Lightweight Steel Strip Defect Detection Algorithm Based on Improved YOLOv8
In order to improve the accuracy of steel strip defect detection and realize the convenient and efficient deployment of the model on the mobile terminal,a lightweight steel strip defect detection method is proposed based on improved YOLOv8n.Firstly,GhostNet is introduced to replace the traditional convolutional layers in the network,significantly reducing the computational burden of the network,by adding the CA(coordinate attention)attention mechanism,the feature extraction ability of the network is effectively enhanced and the receptive field of the model is enhanced.Secondly,in the feature fusion part,the lightweight upsampling module CARAFE(content-aware reassembly of features)is selected to further improve the feature extraction effect of the model.Finally,in order to optimize the performance of network bounding box regression,the Wise-IoU boundary loss function is used to replace the original loss function.The improved strip surface defect detection method is used to conduct experiments on NEU-DET data set.The results show that the parameter number and calculation amount of the improved method are 9.5 M and 6.4 GFLOPs,respectively,which are 15%and 21%higher than those of the original network,and the mAP is 81.5%,which is 3.2%higher.It is superior to other compared target detection algorithms,which can provide reference for the deployment and application of mobile terminal detection equipment.
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