基于改进YOLOv5s的激光软钎焊焊点缺陷检测算法  

Algorithm for Detecting Laser Soldering Point Defect Based on Improved YOLOv5s

作　　者：严蓬辉 陈绪兵[1] 彭伊丽 谢发东 Yan Penghui;Chen Xubing;Peng Yili;Xie Fadong(School of Mechanical and Electrical Engineering,Wuhan Institute of Technology,Wuhan 430205,Hubei,China)

机构地区：[1]武汉工程大学机电工程学院,湖北武汉430205

出　　处：《激光与光电子学进展》2024年第8期209-218,共10页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(52205536);武汉工程大学研究生教育创新基金(CX2022075)。

摘　　要：针对现有激光软钎焊流水线上焊点缺陷检测设备成本高和传统算法检测速度慢的问题,提出了一种能部署在激光软钎焊设备上的改进YOLOv5s焊点缺陷检测算法。通过引入GhostNetV2卷积方式对骨干网络进行轻量化改进,减少了原网络模型的参数量,提高了网络模型的检测速度;同时引入全维动态卷积模块,提升了网络模型的特征提取能力,提高了网络模型的检测精度。实验结果表明:对YOLOv5s模型进行改进后,其网络参数量较原模型下降了23.89%;模型在自制的激光软钎焊焊点缺陷数据集和验证集上的均值平均精度达到了95.0%,相比原模型提高了1个百分点;实验平台上检测速度较原模型提高了12.62 frame/s。最后,在激光软钎焊设备上部署了所提算法,设备基本能够检测出相应的焊点缺陷,并且运行速度达到42.2 frame/s,基本达到了激光软钎焊实时焊点缺陷检测的应用需求。To address the high cost of detection equipment and slow detection speed of traditional algorithms for detecting point defects in laser soldering on the production line,we propose an improved YOLOv5s algorithm that can directly detect defects on the laser soldering equipment.By introducing GhostNetV2 convolution mechanism,the backbone network is lightweight improved,the parameter quantity of the original network model reduced and the detection speed increased.Simultaneously,omnidimensional dynamic convolution module is used to improve both the feature extraction capability and detection accuracy of the model.The experimental results show that the improved YOLOv5s model has a reduced network parameter quantity of 23.89%compared to the original model.The mean average precision of improved model reached 95.0%on the selfmade laser soldering point defect dataset and validation set,reflecting a 1 percentage point improvement over the original model.The detection rate increased by 12.62 frame/s on the experimental platform compared to the original model.Finally,the proposed algorithm is deployed on the laser soldering equipment and can detect corresponding soldering defects at a running speed of 42.2 frame/s,basically meet the realtime welding defect detection needs of laser soldering.

关 键 词：YOLOv5s 激光软钎焊焊点缺陷检测 深度学习 轻量化 全维动态卷积 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TN249[自动化与计算机技术—控制科学与工程]