Transactions of Materials and Heat Treatment

Title：Surface defect detection method on cold rolled sheet metal based on E-YOLO

Authors： Chen Dong1 Liu Xinyi2 3 4 Qi Zhentao1 Yang Peiqing1 Shen Zhen1

Unit： 1.Department of Equipment Manufacturing Engineering Jinan Technician College 2. School of Mechanical and Equipment Engineering Hebei University of Engineering 3. Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province 4. Collaborative Innovation Center for Modern Equipment Manufacturing of Jinan New Area (Hebei)

KeyWords： cold rolled sheet metal surface defect detection deep learning YOLO lightweight model

ClassificationCode：TH113.1；TG335.5

year,vol(issue):pagenumber：2025,50(2):125-131

Abstract：

In practical manufacturing systems, surface defects on the cold rolled sheet metal can lead to a decrease in material strength and pose a safety risk, while the accuracy and stability of traditional detection methods are difficult to guarantee. Therefore, a lightweight and efficient defect detection model E-YOLO (Efficient-YOLO) was proposed, and in response to the shortcomings of the YOLOv8 model in small target detection, feature extraction efficiency and model inference speed, the structural innovations and optimizations about YOLOv8 model were conducted. The inefficient neck connection structure of the original model was modified, the perception ability of the model for subtle defect features was enhanced by introducing a multi-branch feature fusion mechanism and innovatively adopting a feature re-extraction structure. Finally，experiments show that compared with YOLOv8, E-YOLO improves the detection accuracy by 7.3% and increases the detection speed by approximately 18 times compared with the larger model Faster RCNN, providing a feasible way for efficient and accurate detection of surface defects on cold rolled sheet metal.

Funds：

河北省专精特新小巨人企业科技特派团资助项目（SJ240140123）；河北工程大学创新基金项目（SJ2401002049）

作者简介：陈栋(1991-)，男，硕士，讲师,E-mail：2313030095@qq.com;通信作者：刘欣宜(1993-)，女，博士，讲师,E-mail：liuxy93@126.com

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