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

    • “钢材缺陷检测领域取得重要突破。针对钢材表面缺陷形态多样、结构复杂等问题，研究者提出了一种轻量化的VTG-YOLOv7-tiny检测算法。该算法通过设计VoVGA-FPN网络，增强特征融合能力；构建三重坐标注意力机制，提升特征提取能力；引入鬼影混洗卷积，降低模型参数量和计算量；增加大目标检测层，改善检测精度。实验验证表明，改进后的算法在NEU-DET和Severstal数据集上mAP分别提升5.7%和8.5%，参数量和计算量分别降低0.61M和4.2G，精确度和召回率也显著提升。这一成果为钢材缺陷检测提供了新的解决方案，并有望为边缘终端设备提供高效、准确的检测能力。”
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    • Optics and Precision Engineering   Vol. 32, Issue 8, Pages: 1227-1240(2024)

    • DOI：10.37188/OPE.20243208.1227    

      CLC： TP391.4

    • Published：25 April 2024，

      Received：16 October 2023，

      Revised：01 December 2023，

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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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