Hui Zhang, T. Shi, Shi-Qiang He, Haizhou Wang, Feng Ruan
{"title":"基于PM反向扩散的塑料输液组合容器视觉检测系统设计","authors":"Hui Zhang, T. Shi, Shi-Qiang He, Haizhou Wang, Feng Ruan","doi":"10.1109/IHMSC.2015.231","DOIUrl":null,"url":null,"abstract":"Aimed at the defect of black spots, hair, bubbles in medical PP infusion, infusion defect detection system based on machine vision is proposed. Firstly, r design the mechanical actuators, electrical control, and image acquisition system, then use reverse PM diffusion algorithm to enhance the defect area, extracting this area by difference after binarization, and filter the image. Secondly, SVM is used to classify defects and the defective area automatically. Meanwhile, in order to improve the performance of the classifier, the paper selected the best classification parameters based cross validation. The results show that the method is high detection accuracy and requires less training samples, applies to different defect types with accuracy rate of 95%.","PeriodicalId":6592,"journal":{"name":"2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics","volume":"45 1 1","pages":"306-310"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Visual Detection System Design for Plastic Infusion Combinations Containers Based on Reverse PM Diffusion\",\"authors\":\"Hui Zhang, T. Shi, Shi-Qiang He, Haizhou Wang, Feng Ruan\",\"doi\":\"10.1109/IHMSC.2015.231\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aimed at the defect of black spots, hair, bubbles in medical PP infusion, infusion defect detection system based on machine vision is proposed. Firstly, r design the mechanical actuators, electrical control, and image acquisition system, then use reverse PM diffusion algorithm to enhance the defect area, extracting this area by difference after binarization, and filter the image. Secondly, SVM is used to classify defects and the defective area automatically. Meanwhile, in order to improve the performance of the classifier, the paper selected the best classification parameters based cross validation. The results show that the method is high detection accuracy and requires less training samples, applies to different defect types with accuracy rate of 95%.\",\"PeriodicalId\":6592,\"journal\":{\"name\":\"2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics\",\"volume\":\"45 1 1\",\"pages\":\"306-310\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IHMSC.2015.231\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IHMSC.2015.231","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Visual Detection System Design for Plastic Infusion Combinations Containers Based on Reverse PM Diffusion
Aimed at the defect of black spots, hair, bubbles in medical PP infusion, infusion defect detection system based on machine vision is proposed. Firstly, r design the mechanical actuators, electrical control, and image acquisition system, then use reverse PM diffusion algorithm to enhance the defect area, extracting this area by difference after binarization, and filter the image. Secondly, SVM is used to classify defects and the defective area automatically. Meanwhile, in order to improve the performance of the classifier, the paper selected the best classification parameters based cross validation. The results show that the method is high detection accuracy and requires less training samples, applies to different defect types with accuracy rate of 95%.