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Improving the lightweight VTG-YOLOv7-tiny for steel surface defect detection
Information Sciences | 更新时间:2024-05-08
    • Improving the lightweight VTG-YOLOv7-tiny for steel surface defect detection

    • Significant breakthroughs have been made in the field of steel defect detection. Researchers have proposed a lightweight VTG-YOLOv7 tiny detection algorithm to address the diverse forms and complex structures of surface defects in steel. This algorithm enhances feature fusion capability by designing a VoVGA FPN network; Building a triple coordinate attention mechanism to enhance feature extraction capability; Introducing ghosting mixed convolution to reduce the number of model parameters and computational complexity; Add a large target detection layer to improve detection accuracy. Experimental verification shows that the improved algorithm improves mAP by 5.7% and 8.5% on NEU-DET and Severstal datasets, reduces parameter and computational complexity by 0.61M and 4.2G, respectively, and significantly improves accuracy and recall. This achievement provides a new solution for steel defect detection and is expected to provide efficient and accurate detection capabilities for edge terminal equipment.
    • Optics and Precision Engineering   Vol. 32, Issue 8, Pages: 1227-1240(2024)
    • DOI:10.37188/OPE.20243208.1227    

      CLC: TP391.4
    • Received:16 October 2023

      Revised:01 December 2023

      Published:25 April 2024

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  • LIANG Liming,LONG Pengwei,FENG Yao,et al.Improving the lightweight VTG-YOLOv7-tiny for steel surface defect detection[J].Optics and Precision Engineering,2024,32(08):1227-1240. DOI: 10.37188/OPE.20243208.1227.

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