期刊文献+

基于改进YOLOv3算法的线束端子缺陷检测方法

Defect detection method of wire harness terminal based on improved YOLOv3
下载PDF
导出
摘要 研发了一套基于深度学习的汽车线束缺陷检测系统。基于YOLOv3改进的Pr-YOLOv3算法来检测线束端子接插件缺陷,将主干提取网络替换成ResNet50,提高特征提取能力,减少参数量,吸收多尺度预测方式和特征融合方面的优势,将主干提取网络与FPN特征金字塔进行对接,丰富了特征的表达能力。用改进的YOLOv3模型进行训练,准确率可达98.61%,Recall指数可达98.6%。 A deep-learning based automotive wiring harnesses defect detection system is developed.The Pr-YOLOv3 algorithm based on improved YOLOv3 is used to detect defects in wiring harness terminal connectors,and the backbone extraction network is replaced with ResNet50,which improves the feature extraction capability and reduces the number of parameters.Drawing on the advantages in multi-scale prediction methods and feature fusion,the backbone extraction network is interfaced with the FPN feature pyramid,which enriches the feature expression ability.Trained with the improved YOLOv3 model,the accuracy can reach 98.61%and the Recall index can reach 98.6%.
作者 程晓颖 李海生 吕旭波 Cheng Xiaoying;Li Haisheng;Lv Xubo(School of Mechanical Engineering,University of Electronic Science and Technology of China,Chengdu,Sichuan 611731,China;School of Mechanical Engineering,Zhejiang Sci-tech University;Zhejiang SIMITEK Auto-Electronics CO.,LTD)
出处 《计算机时代》 2023年第12期29-33,共5页 Computer Era
关键词 射线无损检测 线束端子缺陷检测 卷积神经网络 YOLOv3 radiographic non-destructive testing wire harness terminal defect detection convolution neural network YOLOv3
  • 相关文献

参考文献3

二级参考文献4

共引文献10

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部