{"title":"基于计算机视觉的梨外观质量分级系统","authors":"Zhongzhi Han, Jing Liu, Yougang Zhao, Yanzhao Li","doi":"10.1109/ICSAI.2012.6223414","DOIUrl":null,"url":null,"abstract":"In order to realize the automatic grading system of pear's appearance quality based on computer vision, a grading system is constructed which includes hardware and software environment. It includes transport unit, control module, image acquisition module, and image processing and recognition module. It can realize automatic feature extraction. The features include shape, color and defect. According to the influence of spots on pear's surface on the defect detection, a spot removal method based on V component's dynamic threshold is put forward. According to the national standards, grading rules of fruit type and defect levels based on above features are constructed. Furthermore grading model of pear based on artificial neural network is also constructed. The recognition rate of 630 images of pear reaches up to 90.3%. The system, equipment and method in this paper have positive significance to on-line grading of pear's quality of appearance.","PeriodicalId":90521,"journal":{"name":"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ICSAI.2012.6223414","citationCount":"2","resultStr":"{\"title\":\"Grading system of pear's appearance quality based on computer vision\",\"authors\":\"Zhongzhi Han, Jing Liu, Yougang Zhao, Yanzhao Li\",\"doi\":\"10.1109/ICSAI.2012.6223414\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to realize the automatic grading system of pear's appearance quality based on computer vision, a grading system is constructed which includes hardware and software environment. It includes transport unit, control module, image acquisition module, and image processing and recognition module. It can realize automatic feature extraction. The features include shape, color and defect. According to the influence of spots on pear's surface on the defect detection, a spot removal method based on V component's dynamic threshold is put forward. According to the national standards, grading rules of fruit type and defect levels based on above features are constructed. Furthermore grading model of pear based on artificial neural network is also constructed. The recognition rate of 630 images of pear reaches up to 90.3%. The system, equipment and method in this paper have positive significance to on-line grading of pear's quality of appearance.\",\"PeriodicalId\":90521,\"journal\":{\"name\":\"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-05-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1109/ICSAI.2012.6223414\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSAI.2012.6223414\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE International Conference on Systems Biology : [proceedings]. IEEE International Conference on Systems Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSAI.2012.6223414","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Grading system of pear's appearance quality based on computer vision
In order to realize the automatic grading system of pear's appearance quality based on computer vision, a grading system is constructed which includes hardware and software environment. It includes transport unit, control module, image acquisition module, and image processing and recognition module. It can realize automatic feature extraction. The features include shape, color and defect. According to the influence of spots on pear's surface on the defect detection, a spot removal method based on V component's dynamic threshold is put forward. According to the national standards, grading rules of fruit type and defect levels based on above features are constructed. Furthermore grading model of pear based on artificial neural network is also constructed. The recognition rate of 630 images of pear reaches up to 90.3%. The system, equipment and method in this paper have positive significance to on-line grading of pear's quality of appearance.