An Algorithm for Detecting Surface Defects in Steel Strips Based on an Improved Lightweight Network

被引：0
作者：
Zhan D. [1 ]
Wang H. [1 ]
Yang X. [1 ]
Ou W. [1 ]
Huang R. [1 ]
Lin J. [1 ]
Yi K. [1 ]
Zhou B. [1 ]
机构：
[1] School of Mechanical and Electrical Engineering, Guangdong University of Technology, Guangzhou
来源：
Defect and Diffusion Forum | 2024年 / 431卷
关键词：
Attention mechanism; Lightweight network; Object detection; Surface defects; YOLOv5;
D O I：
10.4028/p-FOi56w
中图分类号：
学科分类号：
摘要：
In recent years, surface defect detection methods based on deep learning have been widely applied to steel plate surface defect detection. By locating and classifying defects on the surface of steel plates, production efficiency can be improved. However, there is still a conflict between speed and accuracy in the defect detection process. To address this issue, we propose a high-precision, low-latency surface defect detection algorithm called the GhostConv-ECA-YOLOv5 Network (GEA-Net). The GEA-Net model can predict defect categories without compromising classification and detection accuracy. Experimental results show that our proposed improved model has higher performance compared to other comparative models, achieving a 75.6% mAP on the NEU-DET dataset. © 2024 Trans Tech Publications Ltd, Switzerland.
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页码：107 / 114
页数：7
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