Kang Yan, Wenyu Si, Jin Hang, Hong Zhou, Quanyin Zhu
{"title":"基于深度学习的多标签垃圾图像分类","authors":"Kang Yan, Wenyu Si, Jin Hang, Hong Zhou, Quanyin Zhu","doi":"10.1109/DCABES50732.2020.00047","DOIUrl":null,"url":null,"abstract":"In recent years, with the development of deep learning technology, the accuracy of image recognition has been significantly improved. Deep learning has been widely used in the recognition of single-label images. This project aims to intelligently classify domestic garbage images as application scenarios based on depth. Learn to carry out multi-label classification research on images containing multiple visual objects, and design and build a multi-label garbage image classification model to improve recognition accuracy and speed as the main research goal to conduct classification research on multi-label garbage images.","PeriodicalId":351404,"journal":{"name":"2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Multi-label Garbage Image Classification Based on Deep Learning\",\"authors\":\"Kang Yan, Wenyu Si, Jin Hang, Hong Zhou, Quanyin Zhu\",\"doi\":\"10.1109/DCABES50732.2020.00047\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, with the development of deep learning technology, the accuracy of image recognition has been significantly improved. Deep learning has been widely used in the recognition of single-label images. This project aims to intelligently classify domestic garbage images as application scenarios based on depth. Learn to carry out multi-label classification research on images containing multiple visual objects, and design and build a multi-label garbage image classification model to improve recognition accuracy and speed as the main research goal to conduct classification research on multi-label garbage images.\",\"PeriodicalId\":351404,\"journal\":{\"name\":\"2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)\",\"volume\":\"63 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DCABES50732.2020.00047\",\"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 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DCABES50732.2020.00047","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi-label Garbage Image Classification Based on Deep Learning
In recent years, with the development of deep learning technology, the accuracy of image recognition has been significantly improved. Deep learning has been widely used in the recognition of single-label images. This project aims to intelligently classify domestic garbage images as application scenarios based on depth. Learn to carry out multi-label classification research on images containing multiple visual objects, and design and build a multi-label garbage image classification model to improve recognition accuracy and speed as the main research goal to conduct classification research on multi-label garbage images.