File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/TITS.2018.2886283
- Scopus: eid_2-s2.0-85059443403
- WOS: WOS:000505522400017
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Traffic Sign Detection Using a Multi-Scale Recurrent Attention Network
Title | Traffic Sign Detection Using a Multi-Scale Recurrent Attention Network |
---|---|
Authors | |
Keywords | Feature extraction Convolution Object detection Task analysis Image color analysis |
Issue Date | 2019 |
Publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979 |
Citation | IEEE Transactions on Intelligent Transportation Systems, 2019, v. 20 n. 12, p. 4466-4475 How to Cite? |
Abstract | Traffic sign detection plays an important role in intelligent transportation systems. But traffic signs are still not well-detected by deep convolution neural network-based methods because the sizes of their feature maps are constrained, and the environmental context information has not been fully exploited by other researchers. What we need is a way to incorporate relevant context detail from the neighboring layers into the detection architecture. We have developed a novel traffic sign detection approach based on recurrent attention for multi-scale analysis and use of local context in the image. Experiments on the German traffic sign detection benchmark and the Tsinghua-Tencent 100K data set demonstrated that our approach obtained an accuracy comparable to the state-of-the-art approaches in traffic sign detection. |
Persistent Identifier | http://hdl.handle.net/10722/284236 |
ISSN | 2023 Impact Factor: 7.9 2023 SCImago Journal Rankings: 2.580 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Tian, Y | - |
dc.contributor.author | Gelernter, J | - |
dc.contributor.author | WANG, X | - |
dc.contributor.author | LI, J | - |
dc.contributor.author | Yu, Y | - |
dc.date.accessioned | 2020-07-20T05:57:08Z | - |
dc.date.available | 2020-07-20T05:57:08Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | IEEE Transactions on Intelligent Transportation Systems, 2019, v. 20 n. 12, p. 4466-4475 | - |
dc.identifier.issn | 1524-9050 | - |
dc.identifier.uri | http://hdl.handle.net/10722/284236 | - |
dc.description.abstract | Traffic sign detection plays an important role in intelligent transportation systems. But traffic signs are still not well-detected by deep convolution neural network-based methods because the sizes of their feature maps are constrained, and the environmental context information has not been fully exploited by other researchers. What we need is a way to incorporate relevant context detail from the neighboring layers into the detection architecture. We have developed a novel traffic sign detection approach based on recurrent attention for multi-scale analysis and use of local context in the image. Experiments on the German traffic sign detection benchmark and the Tsinghua-Tencent 100K data set demonstrated that our approach obtained an accuracy comparable to the state-of-the-art approaches in traffic sign detection. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979 | - |
dc.relation.ispartof | IEEE Transactions on Intelligent Transportation Systems | - |
dc.rights | IEEE Transactions on Intelligent Transportation Systems. Copyright © Institute of Electrical and Electronics Engineers. | - |
dc.rights | ©20xx 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. | - |
dc.subject | Feature extraction | - |
dc.subject | Convolution | - |
dc.subject | Object detection | - |
dc.subject | Task analysis | - |
dc.subject | Image color analysis | - |
dc.title | Traffic Sign Detection Using a Multi-Scale Recurrent Attention Network | - |
dc.type | Article | - |
dc.identifier.email | Yu, Y: yzyu@cs.hku.hk | - |
dc.identifier.authority | Yu, Y=rp01415 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TITS.2018.2886283 | - |
dc.identifier.scopus | eid_2-s2.0-85059443403 | - |
dc.identifier.hkuros | 310934 | - |
dc.identifier.volume | 20 | - |
dc.identifier.issue | 12 | - |
dc.identifier.spage | 4466 | - |
dc.identifier.epage | 4475 | - |
dc.identifier.isi | WOS:000505522400017 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 1524-9050 | - |