{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T14:17:04Z","timestamp":1779286624198,"version":"3.51.4"},"reference-count":41,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,7,18]],"date-time":"2018-07-18T00:00:00Z","timestamp":1531872000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2017YFC1405600"],"award-info":[{"award-number":["2017YFC1405600"]}]},{"name":"Innovative Research Groups of the Natural science Foundation of China","award":["61621005"],"award-info":[{"award-number":["61621005"]}]},{"name":"Foreign Scholars  in University Research and Teaching Program  (the 111 Project)","award":["No. B18039"],"award-info":[{"award-number":["No. B18039"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, terahertz imaging systems and techniques have been developed and have gradually become a leading frontier field. With the advantages of low radiation and clothing-penetrable, terahertz imaging technology has been widely used for the detection of concealed weapons or other contraband carried on personnel at airports and other secure locations. This paper aims to detect these concealed items with deep learning method for its well detection performance and real-time detection speed. Based on the analysis of the characteristics of terahertz images, an effective detection system is proposed in this paper. First, a lots of terahertz images are collected and labeled as the standard data format. Secondly, this paper establishes the terahertz classification dataset and proposes a classification method based on transfer learning. Then considering the special distribution of terahertz image, an improved faster region-based convolutional neural network (Faster R-CNN) method based on threshold segmentation is proposed for detecting human body and other objects independently. Finally, experimental results demonstrate the effectiveness and efficiency of the proposed method for terahertz image detection.<\/jats:p>","DOI":"10.3390\/s18072327","type":"journal-article","created":{"date-parts":[[2018,7,19]],"date-time":"2018-07-19T03:50:43Z","timestamp":1531972243000},"page":"2327","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["Terahertz Image Detection with the Improved Faster Region-Based Convolutional Neural Network"],"prefix":"10.3390","volume":"18","author":[{"given":"Jinsong","family":"Zhang","sequence":"first","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjie","family":"Xing","sequence":"additional","affiliation":[{"name":"Xi\u2019an Gaoxin No.1 High School, Xi\u2019an 710075, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengdao","family":"Xing","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6482-0863","authenticated-orcid":false,"given":"Guangcai","family":"Sun","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1109\/22.989974","article-title":"Terahertz technology","volume":"50","author":"Siegel","year":"2002","journal-title":"IEEE Trans. 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