Potholes and traffic signs detection by classifier with vision transformers.

Satti, SK; Rajareddy, GNV; Mishra, K; Gandomi, AH

Potholes and traffic signs detection by classifier with vision transformers.

Satti, SK Rajareddy, GNV Mishra, K Gandomi, AH

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Publisher:: NATURE PORTFOLIO
Publication Type:: Journal Article
Citation:: Sci Rep, 2024, 14, (1), pp. 2215
Issue Date:: 2024-01-26

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Field	Value	Language
dc.contributor.author	Satti, SK
dc.contributor.author	Rajareddy, GNV
dc.contributor.author	Mishra, K
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2024-08-01T03:33:31Z
dc.date.available	2024-01-18
dc.date.available	2024-08-01T03:33:31Z
dc.date.issued	2024-01-26
dc.identifier.citation	Sci Rep, 2024, 14, (1), pp. 2215
dc.identifier.issn	2045-2322
dc.identifier.issn	2045-2322
dc.identifier.uri	http://hdl.handle.net/10453/179904
dc.description.abstract	Detecting potholes and traffic signs is crucial for driver assistance systems and autonomous vehicles, emphasizing real-time and accurate recognition. In India, approximately 2500 fatalities occur annually due to accidents linked to hidden potholes and overlooked traffic signs. Existing methods often overlook water-filled and illuminated potholes, as well as those shaded by trees. Additionally, they neglect the perspective and illuminated (nighttime) traffic signs. To address these challenges, this study introduces a novel approach employing a cascade classifier along with a vision transformer. A cascade classifier identifies patterns associated with these elements, and Vision Transformers conducts detailed analysis and classification. The proposed approach undergoes training and evaluation on ICTS, GTSRDB, KAGGLE, and CCSAD datasets. Model performance is assessed using precision, recall, and mean Average Precision (mAP) metrics. Compared to state-of-the-art techniques like YOLOv3, YOLOv4, Faster RCNN, and SSD, the method achieves impressive recognition with a mAP of 97.14% for traffic sign detection and 98.27% for pothole detection.
dc.format	Electronic
dc.language	eng
dc.publisher	NATURE PORTFOLIO
dc.relation.ispartof	Sci Rep
dc.relation.isbasedon	10.1038/s41598-024-52426-4
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Potholes and traffic signs detection by classifier with vision transformers.
dc.type	Journal Article
utslib.citation.volume	14
utslib.location.activity	England
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Data Science Institute (DSI)
pubs.organisational-group	University of Technology Sydney/Strength - DSI - Data Science Institute
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2024-08-01T03:33:25Z
pubs.issue	1
pubs.publication-status	Published online
pubs.volume	14
utslib.citation.issue	1

Abstract:

Detecting potholes and traffic signs is crucial for driver assistance systems and autonomous vehicles, emphasizing real-time and accurate recognition. In India, approximately 2500 fatalities occur annually due to accidents linked to hidden potholes and overlooked traffic signs. Existing methods often overlook water-filled and illuminated potholes, as well as those shaded by trees. Additionally, they neglect the perspective and illuminated (nighttime) traffic signs. To address these challenges, this study introduces a novel approach employing a cascade classifier along with a vision transformer. A cascade classifier identifies patterns associated with these elements, and Vision Transformers conducts detailed analysis and classification. The proposed approach undergoes training and evaluation on ICTS, GTSRDB, KAGGLE, and CCSAD datasets. Model performance is assessed using precision, recall, and mean Average Precision (mAP) metrics. Compared to state-of-the-art techniques like YOLOv3, YOLOv4, Faster RCNN, and SSD, the method achieves impressive recognition with a mAP of 97.14% for traffic sign detection and 98.27% for pothole detection.

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http://hdl.handle.net/10453/179904