Fabric Surface Defect Detection Technology Based on Improved YOLOv7
In view of the limitations of the current fabric defect detection technology in the textile industry,an improved YOLOv7 algorithm for automatic detection of fabric defects was proposed.Firstly,the SPD-Conv module was introduced into the neck network,which retained the feature discrimination information related to defects during convolution downsampling,and solved the problem of insufficient learning of feature information of small targets in the original network.Secondly,the backbone network of YOLOv7 introduced the CA attention mechanism,which not only took into account the channel attention,but also considered the location information,so as to identify defects more effectively.Finally,WIoU was used as the border loss function to make it more focused on the anchor box of general quality,so as to enhance the generalization ability of YOLOv7.Through experimental comparison,it was found that the mAP value and accuracy of the improved algorithm were 92.28％and 95.65％,which were 2.64％and 4.12％higher than the original YOLOv7 algorithm,respectively,which could meet the requirements of the textile industry in terms of defect detection.
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