Chuangang Wang, Fuqiang Li, Yanfeng Li, Houjin Chen, Xuyang Cao
{"title":"铁路外齿轮缺陷状态检测方法","authors":"Chuangang Wang, Fuqiang Li, Yanfeng Li, Houjin Chen, Xuyang Cao","doi":"10.1109/ICIVC.2018.8492899","DOIUrl":null,"url":null,"abstract":"Gear is an important component in railway. The defect status of the gear affects railway riding quality and safety. In this paper, an automatic and quantitative defect status detecting method for external gear is proposed. First, a two-stage scheme is proposed for the segmentation of the meshing region in the gear tooth. Then adaptive thresholding and shape analysis are combined to detect the surface defects. The proposed method is tested on 140 gear tooth images. The area overlap of the meshing region is 0.87. The defect detection method has better performance than some related approaches.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"58 10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Defect Status Detecting Method for External Gear in Railway\",\"authors\":\"Chuangang Wang, Fuqiang Li, Yanfeng Li, Houjin Chen, Xuyang Cao\",\"doi\":\"10.1109/ICIVC.2018.8492899\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Gear is an important component in railway. The defect status of the gear affects railway riding quality and safety. In this paper, an automatic and quantitative defect status detecting method for external gear is proposed. First, a two-stage scheme is proposed for the segmentation of the meshing region in the gear tooth. Then adaptive thresholding and shape analysis are combined to detect the surface defects. The proposed method is tested on 140 gear tooth images. The area overlap of the meshing region is 0.87. The defect detection method has better performance than some related approaches.\",\"PeriodicalId\":173981,\"journal\":{\"name\":\"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)\",\"volume\":\"58 10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIVC.2018.8492899\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIVC.2018.8492899","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Defect Status Detecting Method for External Gear in Railway
Gear is an important component in railway. The defect status of the gear affects railway riding quality and safety. In this paper, an automatic and quantitative defect status detecting method for external gear is proposed. First, a two-stage scheme is proposed for the segmentation of the meshing region in the gear tooth. Then adaptive thresholding and shape analysis are combined to detect the surface defects. The proposed method is tested on 140 gear tooth images. The area overlap of the meshing region is 0.87. The defect detection method has better performance than some related approaches.