Tomás Serrano-Ramírez, N. D. C. Lozano-Rincón, Arturo Mandujano-Nava, Yosafat Jetsemaní Sámano-Flores
{"title":"Artificial vision system for object classification in real time using Raspberry Pi and a Web camera","authors":"Tomás Serrano-Ramírez, N. D. C. Lozano-Rincón, Arturo Mandujano-Nava, Yosafat Jetsemaní Sámano-Flores","doi":"10.35429/jitc.2021.13.5.20.25","DOIUrl":null,"url":null,"abstract":"Computer vision systems are an essential part in industrial automation tasks such as: identification, selection, measurement, defect detection and quality control in parts and components. There are smart cameras used to perform tasks, however, their high acquisition and maintenance cost is restrictive. In this work, a novel low-cost artificial vision system is proposed for classifying objects in real time, using the Raspberry Pi 3B + embedded system, a Web camera and the Open CV artificial vision library. The suggested technique comprises the training of a supervised classification system of the Haar Cascade type, with image banks of the object to be recognized, subsequently generating a predictive model which is put to the test with real-time detection, as well as the calculation for the prediction error. This seeks to build a powerful vision system, affordable and also developed using free software.","PeriodicalId":143010,"journal":{"name":"Revista Tecnologías de la Información y Comunicaciones","volume":"341 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Revista Tecnologías de la Información y Comunicaciones","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.35429/jitc.2021.13.5.20.25","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Computer vision systems are an essential part in industrial automation tasks such as: identification, selection, measurement, defect detection and quality control in parts and components. There are smart cameras used to perform tasks, however, their high acquisition and maintenance cost is restrictive. In this work, a novel low-cost artificial vision system is proposed for classifying objects in real time, using the Raspberry Pi 3B + embedded system, a Web camera and the Open CV artificial vision library. The suggested technique comprises the training of a supervised classification system of the Haar Cascade type, with image banks of the object to be recognized, subsequently generating a predictive model which is put to the test with real-time detection, as well as the calculation for the prediction error. This seeks to build a powerful vision system, affordable and also developed using free software.