首页|基于改进YOLOv5的焊接件表面缺陷检测

基于改进YOLOv5的焊接件表面缺陷检测

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为提高工业环境下焊接件表面缺陷检测精度与检测效率,提出一种改进YOLOv5 目标检测模型的焊接件表面缺陷检测算法.首先,改进主干网络中的C3 模块,包括引入ConvMixer混合卷积结构及Mish激活函数,并增加Shuffle Attention注意力机制,实现在提高检测精度的同时降低模型复杂度;其次,针对NWD Loss存在的不足加以改进,使其关注更多边界框几何信息;最后,将Neck中的所有标准卷积层更换为GSConv卷积层从而进一步提升网络性能.实验结果表明,改进后网络的mAP达到91.3%,与原始网络相比,提高了 4.8%,并且参数量与计算量分别减少 21.4%和8.9%,检测帧率达到142.9 f/s.改进模型在提高检测精度的同时降低了结构复杂度,满足工业生产中对于焊接件表面缺陷检测要求.
Surface Defect Detection of Weldment Based on Improved YOLOv5
In order to improve the detection accuracy and efficiency of weldment surface defects in indus-trial environment,an improved YOLOv5 target detection model for weldment surface defect detection algo-rithm is proposed.Firstly,the C3 module in the central network is improved,including the introduction of ConvMixer,a mixed convolution structure,and Mish activation function,the Shuffle Attention is also added to the module.The improvement achieves better detection accuracy while reducing the complexity of the model.Besides,in order to overcome the shortcomings of NWD Loss,modification is made to focus more on the geometric information of bounding boxes.Lastly,all the standard convolutional layers in Neck are replaced with GSConv convolutional layers,which further enhances the network performance.Experimental results show that the mean average precision of the improved model reaches 91.3%,which is 4.8%higher than the original network.Besides,the amount of parameters and calculations of the model decrease by 21.4%and 8.9%respectively.And it has a detection frame rate of 142.9 f/s.The improved model not on-ly increases the detection accuracy,but also reduces the structural complexity,which meet the requirements for the detection of weldment surface defects in industrial production.

surface defecttarget detectionYOLOv5GSConvattentional mechanism

沈雯静、张政超、许康伟

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上海理工大学机械工程学院,上海 200093

表面缺陷 目标检测 YOLOv5 GSConv 注意力机制

2024

组合机床与自动化加工技术
大连组合机床研究所 中国机械工程学会生产工程分会

组合机床与自动化加工技术

CSTPCD北大核心
影响因子:0.671
ISSN:1001-2265
年,卷(期):2024.(3)
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