Chang'an Wei, Dandan Song, B. Li, Qiqi Li, Shouda Jiang
{"title":"基于YOLOv5的红外小目标探测网络可信度评估方法研究","authors":"Chang'an Wei, Dandan Song, B. Li, Qiqi Li, Shouda Jiang","doi":"10.1109/ICSMD57530.2022.10058215","DOIUrl":null,"url":null,"abstract":"At present, the credibility evaluation system of infrared small target detection network is not perfect. In this paper, we propose a set of credibility evaluation methods for infrared small target detection networks, including generalization evaluation and robustness evaluation. The generalization evaluation uses the original dataset to test the trained YOLOv5 network, and then obtains test indexes such as accuracy, precision, recall and so on. Robustness evaluation process the dataset with noise, which includes Gaussian noise, salt and pepper noise and random occlusion, test the trained network with the dataset after noise processing, obtain the robustness test index, and evaluate the anti-interference ability of the model.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on credibility evaluation method of infrared small target detection network based on YOLOv5\",\"authors\":\"Chang'an Wei, Dandan Song, B. Li, Qiqi Li, Shouda Jiang\",\"doi\":\"10.1109/ICSMD57530.2022.10058215\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"At present, the credibility evaluation system of infrared small target detection network is not perfect. In this paper, we propose a set of credibility evaluation methods for infrared small target detection networks, including generalization evaluation and robustness evaluation. The generalization evaluation uses the original dataset to test the trained YOLOv5 network, and then obtains test indexes such as accuracy, precision, recall and so on. Robustness evaluation process the dataset with noise, which includes Gaussian noise, salt and pepper noise and random occlusion, test the trained network with the dataset after noise processing, obtain the robustness test index, and evaluate the anti-interference ability of the model.\",\"PeriodicalId\":396735,\"journal\":{\"name\":\"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSMD57530.2022.10058215\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSMD57530.2022.10058215","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on credibility evaluation method of infrared small target detection network based on YOLOv5
At present, the credibility evaluation system of infrared small target detection network is not perfect. In this paper, we propose a set of credibility evaluation methods for infrared small target detection networks, including generalization evaluation and robustness evaluation. The generalization evaluation uses the original dataset to test the trained YOLOv5 network, and then obtains test indexes such as accuracy, precision, recall and so on. Robustness evaluation process the dataset with noise, which includes Gaussian noise, salt and pepper noise and random occlusion, test the trained network with the dataset after noise processing, obtain the robustness test index, and evaluate the anti-interference ability of the model.