光学精密工程2024，Vol.32Issue(8)：1227-1240,14.DOI:10.37188/OPE.20243208.1227
改进轻量化VTG-YOLOv7-tiny的钢材表面缺陷检测
Improving the lightweight VTG-YOLOv7-tiny for steel surface defect detection
摘要
Abstract
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.关键词
缺陷检测/轻量化YOLOv7-tiny/VoVGA-FPN网络/三重坐标注意力/鬼影混洗卷积Key words
defect detection/Lightweight YOLOv7-tiny/VoVGA-FPN network/Triplet Coordinate Attention(TCA)/Ghost Shuffle Convolution(GSConv)分类
信息技术与安全科学引用本文复制引用
梁礼明,龙鹏威,冯耀,卢宝贺..改进轻量化VTG-YOLOv7-tiny的钢材表面缺陷检测[J].光学精密工程,2024,32(8):1227-1240,14.基金项目
国家自然科学基金资助项目(No.51365017,No.6146301) （No.51365017,No.6146301）
江西省自然科学基金资助项目(No.20192BAB205084) （No.20192BAB205084）
江西省教育厅科学技术研究重点项目(No.GJJ170491) （No.GJJ170491）
