基于YOLOv5焊缝图像远程检测系统设计  

Design of Remote Inspection System for Welding Seam Image Based on YOLOv5

作　　者：孙超[1,2] 冯耀龙[1] 章红 李少伟[1] SUN Chao;FENG Yaolong;ZHANG Hong;LI Shaowei(School of Intelligent Manufacturing,Jianghan University,Wuhan 430056,Hubei,China;School of Artifi-cial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074,Hubei,China)

机构地区：[1]江汉大学智能制造学院,湖北武汉430056 [2]华中科技大学人工智能与自动化学院,湖北武汉430074

出　　处：《江汉大学学报（自然科学版）》2023年第4期47-56,共10页Journal of Jianghan University：Natural Science Edition

基　　金：江汉大学校级科研基金资助项目(2022XKZX32);江汉大学大学生科研项目(2022zd129)。

摘　　要：针对焊缝图像传输、储存与检测等问题,设计了一套基于YOLOv5焊缝图像远程检测系统。硬件系统由摄像头模块、电源降压模块、主控单元模块、无线射频模块组成。软件系统由WinForms应用程序开发,以可视化界面的形式将焊缝原图和标注后的图像在监控端显示。该检测系统在YOLOv5网络模型中添加了注意力机制,增强了焊缝特征提取能力;在YOLOv5模型的Neck部分中添加了小目标检测层,增强了模型的泛化能力。基于870张图像对YOLOv5卷积神经网络进行训练,然后使用130张图像测试。实验结果表明,改进后的模型最终mAP值稳定在93.42%,相较于原模型对焊缝的检测精度提升了0.53%。Aiming at the problems of welding seam image transmission,storage,and detection,this research designed a remote welding seam image detection system based on YOLOv5.The hardware system consisted of the camera module,power step-down module,main control unit module,and radio frequency module.The software system was developed by the WinForms application program,and the original welding seam image and marked image were displayed on the monitoring end in the form of a visual interface.In this study,an attention mechanism was added to the YOLOv5 network model to enhance the ability of weld seam feature extraction.A small target detection layer was added to the Neck part of the YOLOv5 model to enhance the generalization ability of the model.The YOLOv5 convolution neural network was trained with 870 images and tested with 130 images.The experimental results showed that the mAP value of the improved model was finally stable at 93.42%,0.53%higher than that of the original model.

关 键 词：目标检测 YOLOv5 深度学习 注意力机制 WinForms 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]