{"title":"基于机器视觉和深度学习的包装缺陷检测系统","authors":"J. Sa, Zhihao Li, Qijun Yang, Xuan Chen","doi":"10.1109/ICCCS49078.2020.9118413","DOIUrl":null,"url":null,"abstract":"Detecting packaging defection with high accuracy and efficiency is of great significance in product quality. We use OpenCV to preprocess images which come from damaged package according to characteristics of the image. The processed data is combined with deep learning and based on neural network model ResNet. Meanwhile the processed image data is sent to a convolutional neural network (CNN) for model training. We establish a detection system for product packaging. The detection system provides a solution for automatic detection of package defection, which realizes rapid and accurate detection of product packaging.","PeriodicalId":105556,"journal":{"name":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","volume":"50 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Packaging Defect Detection System Based on Machine Vision and Deep Learning\",\"authors\":\"J. Sa, Zhihao Li, Qijun Yang, Xuan Chen\",\"doi\":\"10.1109/ICCCS49078.2020.9118413\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Detecting packaging defection with high accuracy and efficiency is of great significance in product quality. We use OpenCV to preprocess images which come from damaged package according to characteristics of the image. The processed data is combined with deep learning and based on neural network model ResNet. Meanwhile the processed image data is sent to a convolutional neural network (CNN) for model training. We establish a detection system for product packaging. The detection system provides a solution for automatic detection of package defection, which realizes rapid and accurate detection of product packaging.\",\"PeriodicalId\":105556,\"journal\":{\"name\":\"2020 5th International Conference on Computer and Communication Systems (ICCCS)\",\"volume\":\"50 1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 5th International Conference on Computer and Communication Systems (ICCCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCCS49078.2020.9118413\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 5th International Conference on Computer and Communication Systems (ICCCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCS49078.2020.9118413","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Packaging Defect Detection System Based on Machine Vision and Deep Learning
Detecting packaging defection with high accuracy and efficiency is of great significance in product quality. We use OpenCV to preprocess images which come from damaged package according to characteristics of the image. The processed data is combined with deep learning and based on neural network model ResNet. Meanwhile the processed image data is sent to a convolutional neural network (CNN) for model training. We establish a detection system for product packaging. The detection system provides a solution for automatic detection of package defection, which realizes rapid and accurate detection of product packaging.