dc.contributor.author | Xiao, Hong | |
dc.contributor.author | Zhang, Rongyue | |
dc.contributor.author | Wang, Hao | |
dc.contributor.author | Zhu, Feng | |
dc.contributor.author | Zhang, Cheng | |
dc.contributor.author | Dai, Hong-Ning | |
dc.contributor.author | Zhou, Yubin | |
dc.date.accessioned | 2019-05-03T08:37:31Z | |
dc.date.available | 2019-05-03T08:37:31Z | |
dc.date.created | 2019-01-17T18:27:31Z | |
dc.date.issued | 2018 | |
dc.identifier.isbn | 978-1-5386-9380-3 | |
dc.identifier.uri | http://hdl.handle.net/11250/2596412 | |
dc.description.abstract | Terahertz human body security images have low resolution and a low signal-to-noise ratio. In the traditional method, image segmentation, positioning, and identification are applied to detect objects carried by humans in the THz images. However, it is difficult to satisfy the requirements of detection accuracy and speed with this approach. The current research presents a faster detection framework (R-PCNN) combining the preprocessing and structure optimization of Faster R-CNN. The experiment results show that this method can effectively improve the accuracy and speed of object detection in human body THz images. A detection accuracy of 84.5% can be achieved in dense flow scenes, with an average detection time of less than 20 milliseconds for each image. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | nb_NO |
dc.relation.ispartof | 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovations | |
dc.title | R-PCNN Method to Rapidly Detect Objects on THz Images in Human Body Security Checks | nb_NO |
dc.type | Chapter | nb_NO |
dc.description.version | acceptedVersion | nb_NO |
dc.source.pagenumber | 1777-1782 | nb_NO |
dc.identifier.doi | 10.1109/SmartWorld.2018.00300 | |
dc.identifier.cristin | 1659755 | |
dc.description.localcode | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | nb_NO |
cristin.unitcode | 194,63,55,0 | |
cristin.unitname | Institutt for IKT og realfag | |
cristin.ispublished | true | |
cristin.fulltext | preprint | |
cristin.qualitycode | 1 | |