Industrial Product Surface Defect Detection of Improved YOLOv5

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Industrial Product Surface Defect Detection of Improved YOLOv5

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[1]LIU Zhaoying,CHEN Zhiyuan,ZHANG Ting,et al.Industrial Product Surface Defect Detection of Improved YOLOv5[J].Journal of Zhengzhou University (Engineering Science),2025,46(05):18-25.[doi:10.13705/j.issn.1671-6833.2025.02.020]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 46 Number of periods: 2025 05 Page number: 18-25 Column: Public date: 2025-08-10

Title:: Industrial Product Surface Defect Detection of Improved YOLOv5

Author(s):: LIU Zhaoying¹; CHEN Zhiyuan¹; ZHANG Ting¹; SHI Yanan²; CHEN Yingchun³; 1.College of Computer Science, Beijing University of Technology, Beijing 100124, China; 2.Xinjiang Key Laboratory of Special Equipment Testing Technology, Xinjiang Uygur Autonomous Region Inspection Institute of Special Equipment, Urumqi 830011,China; 3.College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China

Keywords:: surface defect detection; computer vision; multi-scale feature extraction; attention mechanism; decoupling detection head

CLC:: TP391.4

DOI:: 10.13705/j.issn.1671-6833.2025.02.020

Abstract:: Aiming at the problem of limited resources and low contrast of surface defect images in industrial scenarios, an improved YOLOv5 industrial product surface defect detection method was proposed. This method first introduced a receptive field enhancement module in the backbone network to extract richer visual features from different levels of receptive fields. Secondly, a shuffle attention module was added to the feature fusion network to more effectively fuse feature maps of different dimensions. Finally, a task decoupling detection head was adopted, allowing the classification and regression tasks to use independent networks for prediction, reducing mutual interference and improving detection accuracy. The experimental results showed that the parameter and computational complexity of this network were lower than models such as YOLOX, YOLOv7, and deformable DETR. On the pipeline Digital Ray (DR) defect image dataset and NEU-DET dataset, the mAP@0.5 were increased by 2.23 percentage points and 2.99 percentage points respectively, balancing the requirements for real-time and accurate defect detection in industrial scenarios.

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Last Update: 2025-09-19