{"title":"基于视觉机制和杂波抑制模型的红外小目标检测方法","authors":"Shiqian Guo, Baohua Zhang, Jinhui Zhu","doi":"10.1145/3468691.3468715","DOIUrl":null,"url":null,"abstract":"Aiming at the problem of high false alarm rate in the detection of dim infrared targets in complex backgrounds, an infrared dim target detection method based on visual mechanism and clutter suppression model is proposed. Firstly, a weighted template is used to roam the image to enhance dim targets and suppress the background, then the image is divided into sub-regions, the local characteristics are used to suppress strong edge clutter. Finally the dim targets are selected according to the classification criteria. Experimental results show that the proposed algorithm can effectively reduce the false alarm rate in complex scenes, and signal-to-clutter ratio and background suppression factor are better than comparison algorithms.","PeriodicalId":112143,"journal":{"name":"Proceedings of the 2021 2nd International Conference on Computing, Networks and Internet of Things","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Infrared Small Target Detection Method Based on Visual Mechanism and Clutter Suppression Model\",\"authors\":\"Shiqian Guo, Baohua Zhang, Jinhui Zhu\",\"doi\":\"10.1145/3468691.3468715\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aiming at the problem of high false alarm rate in the detection of dim infrared targets in complex backgrounds, an infrared dim target detection method based on visual mechanism and clutter suppression model is proposed. Firstly, a weighted template is used to roam the image to enhance dim targets and suppress the background, then the image is divided into sub-regions, the local characteristics are used to suppress strong edge clutter. Finally the dim targets are selected according to the classification criteria. Experimental results show that the proposed algorithm can effectively reduce the false alarm rate in complex scenes, and signal-to-clutter ratio and background suppression factor are better than comparison algorithms.\",\"PeriodicalId\":112143,\"journal\":{\"name\":\"Proceedings of the 2021 2nd International Conference on Computing, Networks and Internet of Things\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-05-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2021 2nd International Conference on Computing, Networks and Internet of Things\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3468691.3468715\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2021 2nd International Conference on Computing, Networks and Internet of Things","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3468691.3468715","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Infrared Small Target Detection Method Based on Visual Mechanism and Clutter Suppression Model
Aiming at the problem of high false alarm rate in the detection of dim infrared targets in complex backgrounds, an infrared dim target detection method based on visual mechanism and clutter suppression model is proposed. Firstly, a weighted template is used to roam the image to enhance dim targets and suppress the background, then the image is divided into sub-regions, the local characteristics are used to suppress strong edge clutter. Finally the dim targets are selected according to the classification criteria. Experimental results show that the proposed algorithm can effectively reduce the false alarm rate in complex scenes, and signal-to-clutter ratio and background suppression factor are better than comparison algorithms.