重型机械Issue(6)：21-27,7.
基于YOLOv5的换向器表面缺陷检测算法研究
Research on algorithm for detecting commutator surface defect based on YOLOv5
摘要
Abstract
During manufacturing and using mechanical parts,defects may occur on the surface of mechanical parts,during repeated use of components,their minor defects may expand or even cause damage to the com-ponents,leading to malfunction in the system to which they belong.This article takes typical industrial component commutators as the research object and proposes a detection method of component defect based on deep learning algorithm.In this research,based on KolektorSDD data set,firstly,Mosaic data enhancement method was used to rotate and crop the data of commutator defect data set,and then expand the data set and build the data set.Secondly,the constructed data set was divided into training set and testing set,and through YOLOv5 convolution neural networks training model,a commutator surface defect recognition model was established.Finally,the model is used to test the data in the testing set.The results show that the average ac-curacy rate(mAP)and positive sample recall rate(Recall)of the performance evaluation index of the training model are over95%,and the detection accuracy of the commutator surface defects based on YOLOv5 algorithm can reach to 90%.关键词
换向器表面缺陷/深度学习/卷积神经网络/YOLOv5算法Key words
commutator surface defect/deep learning/convolutional neural network/YOLOv5 algorithm分类
信息技术与安全科学引用本文复制引用
张晓丽,马怡琛,仓玉萍,董少飞,郝纳,何思思..基于YOLOv5的换向器表面缺陷检测算法研究[J].重型机械,2023,(6):21-27,7.基金项目
国家自然科学基金(61603324) （61603324）
西安市科技计划项目(21XJZZ0060) （21XJZZ0060）
陕西省表面工程与再制造重点实验室开放基金项目(2022SSER05) （2022SSER05）
西安文理学院交叉建设项目《腐蚀科学与数字信息技术》(XY2023JC01) （XY2023JC01）
陕西省教育厅重点科学研究计划项目(21JS034) （21JS034）
陕西省"四主体一联合"非开挖水平定向勘察智能装备校企联合研究中心、大学生创新创业训练项目(DC2022061,DC2023021) （DC2022061,DC2023021）
