改进轻量化VTG-YOLOv7-tiny的钢材表面缺陷检测OA北大核心CSTPCD

To address the problems of diverse and complex shapes of steel surface defects,detection target missing,and large number of algorithm parameters,a lightweight VTG-YOLOv7-tiny steel defect detec-tion algorithm was proposed.The method first designed VoVGA-FPN network to reduce the loss of infor-mation during information transmission and enhance the network feature fusion ability;second,it con-structed a triple coordinate attention mechanism to improve the model's feature extraction ability of spatial and channel information;third,it introduceed ghost shuffle convolution to reduce the model parameters and computation while improving the accuracy;fourth,it added a large target detection layer to improve the problem that some defects in the feature map occupy a large proportion,resulting in low detection accu-racy.The improved algorithm was verified on the NEU-DET and Severstal steel defect datasets.Com-pared with the original model,the mAP of the improved algorithm is increased by 5.7%and 8.5%,re-spectively;the parameters and computation are reduced by 0.61 M and 4.2 G,respectively;the accuracy and recall are increased by 7.1%,1.8%and 8.9%,7.0%,respectively.The experimental results show that the improved algorithm better balances the detection accuracy and lightweight,and provides a refer-ence for edge terminal devices.