{"title":"模塑件漆面缺陷的鲁棒检测","authors":"Cole Tarry, Michael Stachowsky, M. Moussa","doi":"10.1109/CRV.2014.48","DOIUrl":null,"url":null,"abstract":"A method for detecting local defects in moulded plastic parts is presented. The method uses deflectometry to produce a contrast enhanced image that is later processed in a novel algorithm. The method operates without the need for accurate mechanical models and is robust to changes in image resolution. Experimental results show that the method can detect subtle defects with over 90% accuracy on most parts.","PeriodicalId":385422,"journal":{"name":"2014 Canadian Conference on Computer and Robot Vision","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Robust Detection of Paint Defects in Moulded Plastic Parts\",\"authors\":\"Cole Tarry, Michael Stachowsky, M. Moussa\",\"doi\":\"10.1109/CRV.2014.48\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A method for detecting local defects in moulded plastic parts is presented. The method uses deflectometry to produce a contrast enhanced image that is later processed in a novel algorithm. The method operates without the need for accurate mechanical models and is robust to changes in image resolution. Experimental results show that the method can detect subtle defects with over 90% accuracy on most parts.\",\"PeriodicalId\":385422,\"journal\":{\"name\":\"2014 Canadian Conference on Computer and Robot Vision\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-05-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 Canadian Conference on Computer and Robot Vision\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CRV.2014.48\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 Canadian Conference on Computer and Robot Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CRV.2014.48","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Robust Detection of Paint Defects in Moulded Plastic Parts
A method for detecting local defects in moulded plastic parts is presented. The method uses deflectometry to produce a contrast enhanced image that is later processed in a novel algorithm. The method operates without the need for accurate mechanical models and is robust to changes in image resolution. Experimental results show that the method can detect subtle defects with over 90% accuracy on most parts.