基于改进YOLOv5的铝锭合金表面缺陷检测技术研究
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1.贵州省计量测试院 贵阳 550003; 2.贵州大学电气工程学院 贵阳 550025

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TP391; TG146; TN805

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国家自然科学基金(52265066, 62203132)、贵州省教育厅青年科技人才成长项目(黔教合KY字[2022]138号)、贵州大学博士基金(GDRJ[2020]30)项目资助


Improved YOLOv5-based surface defect detection technology for aluminum ingot alloys
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1.Institute of Metrology and Testing,Guiyang 550003, China; 2.College of Electrical Engineering, Guizhou University,Guiyang 550025, China

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    摘要:

    针对铝锭合金表面缺陷在形态上不规则、检测效果欠佳的问题,提出了一种基于改进YOLOv5的铝锭合金表面缺陷检测方法。首先,利用Res2Net特征提取网络块替换基线模型中的CSPDarknet53模块,以实现多尺度缺陷的有效检测。其次,在YOLOv5的主干网络引入CBAM卷积注意力模块,以增强对缺陷特征的表征能力。最后,使用基于过参数化的重参数化卷积块替代主干和颈部网络的3×3卷积块,以降低模型的推理延时。与传统的目标检测方法进行对比实验,结果表明改进后的方法对缺陷检测的mAP达到75.8%,在检测精度和推理速度上均有显著提升,可很好地满足实际工业生产的任务和需求。

    Abstract:

    Aiming at the problems of irregular morphology and suboptimal detection performance of surface defect on aluminum ingot alloys, an improved YOLOv5-based defect detection method is proposed. Firstly, The Res2Net feature extraction network block is employed to replace the CSPDarknet53 module of the baseline model, which can effectively detect the multi-scale defect. Secondly, the CBAM convolutional attention module is introduced into the backbone network of YOLOv5 to enhance the representational ability of defect features. Finally, the over-parameterized reparameterization convolutional blocks are used to substitute for the 3×3 convolutional blocks in the backbone and neck networks so as to reduce the model′s inference latency. Experimental results compared with the traditional target detection methods demonstrate the improved method achieves a mAP of 75.8% for defect detection, which is a significant improvement both in detection accuracy and inference speed, and can well satisfy the tasks and demands of practical industrial production.

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胡波,王佳欣,杨青,陈婷,杨明.基于改进YOLOv5的铝锭合金表面缺陷检测技术研究[J].电子测量技术,2024,47(14):121-126

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  • 在线发布日期: 2024-11-22
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