{"title":"复杂环境下车辆遥感图像识别的可解释性分析","authors":"Yuxin Huo, Yizhuo Ai, Chengqiang Zhao, Yuanwei Li","doi":"10.1117/12.2653489","DOIUrl":null,"url":null,"abstract":"Deep learning technology has yielded good results in remote sensing image recognition of vehicles, but most existing recognition network models have poor interpretability, which limits its wide application. In order to achieve effective detection and recognition of vehicles in the complex environment, in this paper, the YOLOv4 is adopted to realize remote sensing images for vehicle target recognition. In addition, the optimized interpretation method with LIME is used to interpret the recognition results, improving the credibility of the recognition results.","PeriodicalId":32903,"journal":{"name":"JITeCS Journal of Information Technology and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Interpretable analysis of remote sensing image recognition of vehicles in the complex environment\",\"authors\":\"Yuxin Huo, Yizhuo Ai, Chengqiang Zhao, Yuanwei Li\",\"doi\":\"10.1117/12.2653489\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Deep learning technology has yielded good results in remote sensing image recognition of vehicles, but most existing recognition network models have poor interpretability, which limits its wide application. In order to achieve effective detection and recognition of vehicles in the complex environment, in this paper, the YOLOv4 is adopted to realize remote sensing images for vehicle target recognition. In addition, the optimized interpretation method with LIME is used to interpret the recognition results, improving the credibility of the recognition results.\",\"PeriodicalId\":32903,\"journal\":{\"name\":\"JITeCS Journal of Information Technology and Computer Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JITeCS Journal of Information Technology and Computer Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1117/12.2653489\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JITeCS Journal of Information Technology and Computer Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2653489","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Interpretable analysis of remote sensing image recognition of vehicles in the complex environment
Deep learning technology has yielded good results in remote sensing image recognition of vehicles, but most existing recognition network models have poor interpretability, which limits its wide application. In order to achieve effective detection and recognition of vehicles in the complex environment, in this paper, the YOLOv4 is adopted to realize remote sensing images for vehicle target recognition. In addition, the optimized interpretation method with LIME is used to interpret the recognition results, improving the credibility of the recognition results.