{"title":"基于ResNet50网络的海洋垃圾图像分类","authors":"Miao Dai, Youfu Jiang, Bei Pan","doi":"10.1117/12.2671344","DOIUrl":null,"url":null,"abstract":"In order to improve the efficiency of marine garbage classification, this study first enhances and processes the data set, then uses ResNet50 network model and modifies its lowest layer of network, and finally obtains the accuracy in different cycles through training and verification. The results show that the accuracy of the network model trained in this study is as high as 96% under the most stable cycle.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Classification of marine garbage image based on ResNet50 network\",\"authors\":\"Miao Dai, Youfu Jiang, Bei Pan\",\"doi\":\"10.1117/12.2671344\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to improve the efficiency of marine garbage classification, this study first enhances and processes the data set, then uses ResNet50 network model and modifies its lowest layer of network, and finally obtains the accuracy in different cycles through training and verification. The results show that the accuracy of the network model trained in this study is as high as 96% under the most stable cycle.\",\"PeriodicalId\":120866,\"journal\":{\"name\":\"Artificial Intelligence and Big Data Forum\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Artificial Intelligence and Big Data Forum\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2671344\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Intelligence and Big Data Forum","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2671344","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Classification of marine garbage image based on ResNet50 network
In order to improve the efficiency of marine garbage classification, this study first enhances and processes the data set, then uses ResNet50 network model and modifies its lowest layer of network, and finally obtains the accuracy in different cycles through training and verification. The results show that the accuracy of the network model trained in this study is as high as 96% under the most stable cycle.
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