Research on Optimization of YOLOv5s Detection Algorithm for Steel Surface Defect

被引：1

作者：

Xu, Hongjun ^{[1
]}

Tang, Ziqiang ^{[1
]}

Zhang, Jindong ^{[2
]}

Zhu, Peihua ^{[3
]}

机构：

[1] State Grid Jiangsu Electric Engineering Consulting Co., Ltd., Nanjing

[2] College of Civil Engineering, Tongji University, Shanghai

[3] College of Safety Engineering and Emergency Management, Shijiazhuang Tiedao Universtiy, Shijiazhuang

来源：

Computer Engineering and Applications | 2024年 / 60卷 / 07期

关键词：

attention mechanism; defect detection; feature extraction; spatial pyramid pooling-cross stage partial channel (SPPCSPC); YOLOv5;

D O I：

10.3778/j.issn.1002-8331.2307-0275

中图分类号：

TB3 [工程材料学];

学科分类号：

0805 ; 080502 ;

摘要：

Aiming at the problems that YOLOv5 has insufficient ability to extract complex features of steel defects and the detect results are susceptible to background environment, a steel surface defect detection algorithm based on YOLOv5s is proposed. This algorithm introduces CBAM attention into C3 to enhance attention to key information. It utilizes the CARAFE to replace the nearest neighbor interpolation algorithm, reducing the loss of feature information caused by upsampling. It proposes to replace the SPPF in YOLOv5 with the SPPCPSC, which can improve the expressive ability of the network. The experimental results show that the mAP@0.5 of the proposed YOLOv5s improved model on the NEU-DET dataset reaches 76.6%, which is 2.3 percentage points higher than that of YOLOv5s. The model parameters are basically the same as YOLOv5s. The CARAFE module is the main reason for the slowdown of the improved model detection speed. In addition, the combination of the CARAFE and the SPPCSPC_group has a good effect on the detection accuracy of the model. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.

引用

页码：306 / 314

页数：8

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