{"title":"被动太赫兹辐射计扫描器目标检测与自适应滤波","authors":"Bo Wen, Tzu-kao Wang","doi":"10.1109/CCISP55629.2022.9974473","DOIUrl":null,"url":null,"abstract":"Passive terahertz radiometer scanner [1] is an emerging type of handheld security inspection device that could overcome some of the shortcomings of current security inspection devices on the market. However, subject to several difficulties such as unstable measurements and ambiguous signal features, to detect hidden objects using this device is challenging. The previous research on this topic was insufficient, and the object detection algorithm was less reliable and lacked scientific verifi-cation. In this paper, we propose a whole new pipeline to address this task. We explore and compare a series of adaptive filtering techniques and propose a customized Kalman filter to extract the signal features that describe hidden objects. Then, we adopt two machine learning methods on the filtered signal to detect the hidden objects. Experiment shows that the proposed pipeline can achieve over 85 % accuracy, which hugely outperforms the old methods.","PeriodicalId":431851,"journal":{"name":"2022 7th International Conference on Communication, Image and Signal Processing (CCISP)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Passive THz Radiometer Scanner Object Detection with Adaptive Filtering\",\"authors\":\"Bo Wen, Tzu-kao Wang\",\"doi\":\"10.1109/CCISP55629.2022.9974473\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Passive terahertz radiometer scanner [1] is an emerging type of handheld security inspection device that could overcome some of the shortcomings of current security inspection devices on the market. However, subject to several difficulties such as unstable measurements and ambiguous signal features, to detect hidden objects using this device is challenging. The previous research on this topic was insufficient, and the object detection algorithm was less reliable and lacked scientific verifi-cation. In this paper, we propose a whole new pipeline to address this task. We explore and compare a series of adaptive filtering techniques and propose a customized Kalman filter to extract the signal features that describe hidden objects. Then, we adopt two machine learning methods on the filtered signal to detect the hidden objects. Experiment shows that the proposed pipeline can achieve over 85 % accuracy, which hugely outperforms the old methods.\",\"PeriodicalId\":431851,\"journal\":{\"name\":\"2022 7th International Conference on Communication, Image and Signal Processing (CCISP)\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 7th International Conference on Communication, Image and Signal Processing (CCISP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCISP55629.2022.9974473\",\"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 7th International Conference on Communication, Image and Signal Processing (CCISP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCISP55629.2022.9974473","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Passive THz Radiometer Scanner Object Detection with Adaptive Filtering
Passive terahertz radiometer scanner [1] is an emerging type of handheld security inspection device that could overcome some of the shortcomings of current security inspection devices on the market. However, subject to several difficulties such as unstable measurements and ambiguous signal features, to detect hidden objects using this device is challenging. The previous research on this topic was insufficient, and the object detection algorithm was less reliable and lacked scientific verifi-cation. In this paper, we propose a whole new pipeline to address this task. We explore and compare a series of adaptive filtering techniques and propose a customized Kalman filter to extract the signal features that describe hidden objects. Then, we adopt two machine learning methods on the filtered signal to detect the hidden objects. Experiment shows that the proposed pipeline can achieve over 85 % accuracy, which hugely outperforms the old methods.