Bearing Housing Surface Defect Model Detection Based on Improved YOLOv5
Aiming at the problem that the manual detection efficiency of surface defects of parts(taking bearing housing as an example)is low,and it is easy to misdetect and miss detection,this paper proposes an improved detection algorithm based on YOLOv5.Firstly,the CA attention mechanism is introduced in the backbone network,which embeds the position information into the channel attention to effectively improve the detection performance of the model.Secondly,the location loss function of YOLOv5 is changed from CIoU to WIoU v3 loss to improve the regression accuracy of prediction box.Then,the lightweight convolution method GSConv is used to replace the original standard convolution,which reduces the computation and parameter amount of the network model and improves the model inference speed.Experimental results show that compared with the original YOLOv5s model,the improved algorithm reduces the number of parameters by 6.1%,the amount of computation decreases by 3%,the average detection accuracy is increased by 1.3%,and the detection speed is increased by 1.6%.
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