Traffic Sign Recognition Based on Deep Learning

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aut.relation.journalMultimedia Tools and Applicationsen_NZ
aut.researcherYan, Wei Qi
dc.contributor.authorZhu, Yen_NZ
dc.contributor.authorYan, WQen_NZ
dc.date.accessioned2022-06-21T01:38:03Z
dc.date.available2022-06-21T01:38:03Z
dc.description.abstractIntelligent Transportation System (ITS), including unmanned vehicles, has been gradually matured despite on road. How to eliminate the interference due to various environmental factors, carry out accurate and efficient traffic sign detection and recognition, is a key technical problem. However, traditional visual object recognition mainly relies on visual feature extraction, e.g., color and edge, which has limitations. Convolutional neural network (CNN) was designed for visual object recognition based on deep learning, which has successfully overcome the shortcomings of conventional object recognition. In this paper, we implement an experiment to evaluate the performance of the latest version of YOLOv5 based on our dataset for Traffic Sign Recognition (TSR), which unfolds how the model for visual object recognition in deep learning is suitable for TSR through a comprehensive comparison with SSD (i.e., single shot multibox detector) as the objective of this paper. The experiments in this project utilize our own dataset. Pertaining to the experimental results, YOLOv5 achieves 97.70% in terms of mAP@0.5 for all classes, SSD obtains 90.14% mAP in the same term. Meanwhile, regarding recognition speed, YOLOv5 also outperforms SSD.en_NZ
dc.identifier.citationMultimedia Tools and Applications, 81, 17779–17791 (2022). https://doi.org/10.1007/s11042-022-12163-0
dc.identifier.doi10.1007/s11042-022-12163-0en_NZ
dc.identifier.issn1380-7501en_NZ
dc.identifier.issn1573-7721en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/15247
dc.languageenen_NZ
dc.publisherSpringer Science and Business Media LLCen_NZ
dc.relation.urihttps://link.springer.com/article/10.1007/s11042-022-12163-0
dc.rightsOpen Access This 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.subjectDeep learning; Traffic sign recognition; CNN; YOLOv5; SSD
dc.titleTraffic Sign Recognition Based on Deep Learningen_NZ
dc.typeJournal Article
pubs.elements-id451008
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies
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
pubs.organisational-data/AUT/zAcademic Progression
pubs.organisational-data/AUT/zAcademic Progression/AP - Design and Creative Technologies
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