G-YOLO:An Embedded Small Target Defect Detection Algorithm Based on Improved YOLOv5
An embedded algorithm G-YOLO for end-to-end detection of small defect was proposed to solving problems of low recognition accuracy and large model parameter scale during industrial product defect detection using manual defect detection or deep learning algorithms such as YOLOv5.Firstly,G-YOLO used a double-layer convolution FConv module with a convolution kernel of three and a convolution kernel of one,which improved the problem of large parameter quantities caused by the original single-layer convolution.Secondly,the improved lightweight cross-stage GSP module fused with coordinate attention(CA)mechanism used in the backbone network could enhance features by utilizing redundant information for inexpensive linear operations and concentrating the defect information.Consequently,the network's ability to extract defect features was improved.Finally,the neck of the original YOLOv5 module was removed,the amount of network parameters was reduced and the speed of network detection was improved.The results showed that the embedded algorithm G-YOLO compresses the size of models and improved the effectiveness of defect detection,which better met the requirements of lightweight embedded models.
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