The Improved Framework for Traffic Sign Recognition Using Guided Image Filtering

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aut.relation.articlenumber461en_NZ
aut.relation.issue6en_NZ
aut.relation.journalSN Computer Scienceen_NZ
aut.relation.volume3en_NZ
aut.researcherYan, Wei Qi
dc.contributor.authorXing, Jen_NZ
dc.contributor.authorNguyen, Men_NZ
dc.contributor.authorQi Yan, Wen_NZ
dc.date.accessioned2022-08-30T23:05:58Z
dc.date.available2022-08-30T23:05:58Z
dc.description.abstractIn the lighting conditions such as hazing, raining, and weak lighting condition, the accuracy of traffic sign recognition is not very high due to missed detection or incorrect positioning. In this article, we propose a traffic sign recognition (TSR) algorithm based on Faster R-CNN and YOLOv5. The road signs were detected from the driver’s point of view and the view was assisted by satellite images. First, we conduct image preprocessing by using guided image filtering for the input image to remove noises. Second, the processed image is input into the proposed networks for model training and testing. Three datasets are employed to verify the effectiveness of the proposed method finally. The outcomes of the traffic sign recognition are promising.en_NZ
dc.identifier.citationSN Computer Science. 3, 461 (2022). https://doi.org/10.1007/s42979-022-01355-y
dc.identifier.doi10.1007/s42979-022-01355-yen_NZ
dc.identifier.issn2661-8907en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/15406
dc.languageenen_NZ
dc.publisherSpringer Science and Business Media LLCen_NZ
dc.relation.urihttps://link.springer.com/article/10.1007/s42979-022-01355-yen_NZ
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectTrafc sign recognition; Faster R-CNN; GTSDB dataset; FRIDA database
dc.titleThe Improved Framework for Traffic Sign Recognition Using Guided Image Filteringen_NZ
dc.typeJournal Article
pubs.elements-id475159
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies/School of Engineering, Computer & Mathematical Sciences
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies/PBRF ECMS
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