基于改进的YOLOv5的带式磨削表面缺陷检测
Surface Defect Detection of Belt Grinding Based on Improved YOLOv5
DOI: 10.12677/MOS.2023.125387, PDF,   
作者: 赵霆霆, 周 骅, 赵 麒：贵州大学大数据与信息工程学院，贵州 贵阳
关键词: 深度学习；表面缺陷检测；定量特征；带式磨削；Deep Learning； Surface Defect Detection； Quantitative Feature； Belt Grinding
摘要: 由于带式磨削表面缺陷的复杂性和多样性，表面缺陷的自动识别和定量表征仍然是一个需要解决的问题。为了对带式磨削中的表面缺陷进行检测，提出了一种多分类识别与量化的方法。利用YOLOv5算法进行缺陷检测，得到缺陷的分类和位置信息。首先在模型中引入CBAM注意力机制提升模型对细小缺陷的感知能力，其次对主干网络中的标准卷积替换成深度可分离卷积以降低模型参数量，最后引入SiOU边界损失函数加速模型收敛和提升准确率。最终改进后模型的mAP@0.5达到90.7%。其检测效率高达158.4 FPS，可进行后续实时监控。结果表明，本文提出改进的YOLOv5模型在带式磨削表面缺陷检测上具有可靠性和实时准确性。
Abstract: Due to the complexity and diversity of surface defects in belt grinding, the automatic identification and quantitative characterization of surface defects are still a problem to be solved. In order to de-tect surface defects in belt grinding, a multi-classification recognition and quantification method is proposed. YOLOv5 algorithm is used for defect detection to obtain the classification and location in-formation of defects. Firstly, the CBAM attention mechanism is introduced into the model to im-prove the perception ability of the model for small defects. Secondly, the standard convolution in the backbone network was replaced by the depthwise separable convolution to reduce the amount of model parameters. Finally, the SiOU boundary loss function was introduced to accelerate model convergence and improve accuracy. Finally, the mAP@0.5 of the improved model reaches 90.7%. The detection efficiency is up to 158.4 FPS, which can be used for subsequent real-time monitoring. The results show that the improved YOLOv5 model proposed in this paper has reliability and re-al-time accuracy in belt grinding surface defect detection.
文章引用：赵霆霆, 周骅, 赵麒. 基于改进的YOLOv5的带式磨削表面缺陷检测[J]. 建模与仿真, 2023, 12(5): 4247-4256. https://doi.org/10.12677/MOS.2023.125387

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