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2025 , Vol. 30 >Issue 5: 1037 - 1049

DOI: https://doi.org/10.1007/s12204-024-2723-2

Computing & Computer Technologies

CSC-YOLO: An Image Recognition Model for Surface Defect Detection of Copper Strip and Plates

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  • Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China

Received date: 2023-06-02

  Accepted date: 2023-08-05

  Online published: 2024-04-22

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Abstract

In order to meet the requirements of accurate identification of surface defects on copper strip in industrial production, a detection model of surface defects based on machine vision, CSC-YOLO, is proposed. The model uses YOLOv4-tiny as the benchmark network. First, K-means clustering is introduced into the benchmark network to obtain anchor frames that match the self-built dataset. Second, a cross-region fusion module is introduced in the backbone network to solve the difficult target recognition problem by fusing contextual semantic information. Third, the spatial pyramid pooling-efficient channel attention network (SPP-E) module is introduced in the path aggregation network (PANet) to enhance the extraction of features. Fourth, to prevent the loss of channel information, a lightweight attention mechanism is introduced to improve the performance of the network. Finally, the performance of the model is improved by adding adjustment factors to correct the loss function for the dimensional characteristics of the surface defects. CSC-YOLO was tested on the self-built dataset of surface defects in copper strip, and the experimental results showed that the mAP of the model can reach 93.58%, which is a 3.37% improvement compared with the benchmark network, and FPS, although decreasing compared with the benchmark network, reached 104. CSC-YOLO takes into account the real-time requirements of copper strip production. The comparison experiments with Faster RCNN, SSD300, YOLOv3, YOLOv4, Resnet50-YOLOv4, YOLOv5s, YOLOv7, and other algorithms show that the algorithm obtains a faster computation speed while maintaining a higher detection accuracy.

Cite this article

ZHANG Guo, CHEN Tao, WANG Jianping . CSC-YOLO: An Image Recognition Model for Surface Defect Detection of Copper Strip and Plates[J]. Journal of Shanghai Jiaotong University(Science), 2025 , 30(5) : 1037 -1049 . DOI: 10.1007/s12204-024-2723-2

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