Transactions of Materials and Heat Treatment

Title：Surface defect detection on hot rolled strip steel based on RM-YOLOv8

Authors： Yang Ao1 Hou Hongling1 Zhu Kangkai1 Li Xiangyao1 Zhao Yandi2

Unit： 1. School of Mechanical Engineering Shaanxi University of Technology 2. School of Mechanical and Precision Instrument Engineering Xi′an University of Technology

KeyWords： hot rolled strip steel defect detection deep learning RM-YOLOv8 feature pyramid attention mechanism

ClassificationCode：TP391.4

year,vol(issue):pagenumber：2025,50(2):103-114

Abstract：

For the problems of low detection accuracy, easy to miss detection and misdetect in complex industrial environments caused by traditional algorithms for the surface defect detection on hot rolled strip steel, a surface defect detection algorithm of hot rolled strip steel based on RM-YOLOv8 was proposed, and ConvNeXt Block module was introduced into the feature extraction network to enhance the feature extraction capability of the network. Then, attention mechanism RC_MHSA was constructed to enhance the training of feature extraction network for the regions of interest in feature maps, and a lightweight and learnable visual center module (LLVC) was constructed. Furthermore, the richer feature information was added to the deep network by cross-layer channel fusion to improve the model ability to capture local corner features, and finally a lightweight module C2f_Ghost was constructed by using GhostBottleneck module, which reduced the amount of computation and improved the detection accuracy of the network. The experimental results show that the average mean precision of RM-YOLOv8 on the NEU-DET dataset of surface defects in hot rolled strip steel is increased by 7.0%, the accuracy is increased by 8.9%, and the recall rate is increased by 6.8% compared to the original YOLOv8 network. Compared with other detection algorithms, the proposed RM-YOLOv8 defect detection algorithm has better detection performance and industrial application value.

Funds：

陕西省自然科学基础研究计划项目（2023-JC-YB-452）

作者简介：杨澳(1999-)，男，硕士研究生,E-mail：2075330899@qq.com;通信作者：侯红玲(1976-)，女，博士，教授,E-mail：xjtuhhl@163.com

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