{"title":"基于SIFT算法的小批量生产PCB自动化检测","authors":"Charbel Szymanski, M. Stemmer","doi":"10.1109/ISIE.2015.7281535","DOIUrl":null,"url":null,"abstract":"There has long been a concern to improve the techniques of image processing for PCB's inspection. However, it is noticed the focus is mainly on the inspection of PCB in mass production. Even with a low volume of production, the small series production has become increasingly relevant. This work presents an image processing pipeline for mounted PCB inspection in small series production, strongly based on SIFT (Scale Invariant Feature Transform) algorithm. The defect types covered are: absent, rotated/inverted, misplaced and wrong. Experiments were performed using a software developed with the proposed approach. That software runs in an automatic optical inspection machine with an industrial camera and controlled illumination. The overall accuracy of the inspections is around 80%.","PeriodicalId":377110,"journal":{"name":"2015 IEEE 24th International Symposium on Industrial Electronics (ISIE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"Automated PCB inspection in small series production based on SIFT algorithm\",\"authors\":\"Charbel Szymanski, M. Stemmer\",\"doi\":\"10.1109/ISIE.2015.7281535\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"There has long been a concern to improve the techniques of image processing for PCB's inspection. However, it is noticed the focus is mainly on the inspection of PCB in mass production. Even with a low volume of production, the small series production has become increasingly relevant. This work presents an image processing pipeline for mounted PCB inspection in small series production, strongly based on SIFT (Scale Invariant Feature Transform) algorithm. The defect types covered are: absent, rotated/inverted, misplaced and wrong. Experiments were performed using a software developed with the proposed approach. That software runs in an automatic optical inspection machine with an industrial camera and controlled illumination. The overall accuracy of the inspections is around 80%.\",\"PeriodicalId\":377110,\"journal\":{\"name\":\"2015 IEEE 24th International Symposium on Industrial Electronics (ISIE)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-06-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE 24th International Symposium on Industrial Electronics (ISIE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISIE.2015.7281535\",\"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 IEEE 24th International Symposium on Industrial Electronics (ISIE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISIE.2015.7281535","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automated PCB inspection in small series production based on SIFT algorithm
There has long been a concern to improve the techniques of image processing for PCB's inspection. However, it is noticed the focus is mainly on the inspection of PCB in mass production. Even with a low volume of production, the small series production has become increasingly relevant. This work presents an image processing pipeline for mounted PCB inspection in small series production, strongly based on SIFT (Scale Invariant Feature Transform) algorithm. The defect types covered are: absent, rotated/inverted, misplaced and wrong. Experiments were performed using a software developed with the proposed approach. That software runs in an automatic optical inspection machine with an industrial camera and controlled illumination. The overall accuracy of the inspections is around 80%.