Detection and Recognition of Obscured Traffic Signs During Vehicle Movement

The present study proposes an algorithm to extract obscured traffic sign information from road driving images and fuse it with the vehicle movement process by integrating the vehicle speed. Given the low recognition rate of obscured traffic signs, the proposed algorithm utilizes a traditional color-...
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Autor*in:	Shi Luo [verfasserIn] Chenghang Wu [verfasserIn] Lingen Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Traffic sign recognition image recognition image fusion

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 11(2023), Seite 122516-122525
Übergeordnetes Werk:	volume:11 ; year:2023 ; pages:122516-122525

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2023.3329068

Katalog-ID:	DOAJ098385704

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520			\|a The present study proposes an algorithm to extract obscured traffic sign information from road driving images and fuse it with the vehicle movement process by integrating the vehicle speed. Given the low recognition rate of obscured traffic signs, the proposed algorithm utilizes a traditional color-shape recognition approach to extract potential traffic sign regions. The fusion of traffic sign information from multiple consecutive frames into a single frame enhances the completeness and accuracy of the traffic sign information, bringing it closer to the original characteristics of the traffic sign. The experimental results demonstrate that fusing traffic sign information from the first and third frames improves the template matching similarity by 15.2%. And successfully identifies traffic signs that cannot be recognized by the YOLOV4 and YOLOV8 convolutional neural network when driving at vehicle speeds of <inline-formula< <tex-math notation="LaTeX"<$18\left ({\mathrm {km}/\mathrm {h} }\right)$ </tex-math<</inline-formula<, <inline-formula< <tex-math notation="LaTeX"<$36\left ({\mathrm {km}/\mathrm {h} }\right)$ </tex-math<</inline-formula<, and <inline-formula< <tex-math notation="LaTeX"<$54\left ({\mathrm {km}/\mathrm {h} }\right)$ </tex-math<</inline-formula<. The findings highlight that the proposed algorithm can effectively fuse obscured traffic sign information during vehicle movement to obtain traffic signs with more similar geometric features to the original traffic signs.
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allfields	10.1109/ACCESS.2023.3329068 doi (DE-627)DOAJ098385704 (DE-599)DOAJ3a8fd74667c9456c99f05d2c16c98fea DE-627 ger DE-627 rakwb eng TK1-9971 Shi Luo verfasserin aut Detection and Recognition of Obscured Traffic Signs During Vehicle Movement 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The present study proposes an algorithm to extract obscured traffic sign information from road driving images and fuse it with the vehicle movement process by integrating the vehicle speed. Given the low recognition rate of obscured traffic signs, the proposed algorithm utilizes a traditional color-shape recognition approach to extract potential traffic sign regions. The fusion of traffic sign information from multiple consecutive frames into a single frame enhances the completeness and accuracy of the traffic sign information, bringing it closer to the original characteristics of the traffic sign. The experimental results demonstrate that fusing traffic sign information from the first and third frames improves the template matching similarity by 15.2%. And successfully identifies traffic signs that cannot be recognized by the YOLOV4 and YOLOV8 convolutional neural network when driving at vehicle speeds of <inline-formula< <tex-math notation="LaTeX"<$18\left ({\mathrm {km}/\mathrm {h} }\right)$ </tex-math<</inline-formula<, <inline-formula< <tex-math notation="LaTeX"<$36\left ({\mathrm {km}/\mathrm {h} }\right)$ </tex-math<</inline-formula<, and <inline-formula< <tex-math notation="LaTeX"<$54\left ({\mathrm {km}/\mathrm {h} }\right)$ </tex-math<</inline-formula<. The findings highlight that the proposed algorithm can effectively fuse obscured traffic sign information during vehicle movement to obtain traffic signs with more similar geometric features to the original traffic signs. Traffic sign recognition image recognition image fusion Electrical engineering. Electronics. Nuclear engineering Chenghang Wu verfasserin aut Lingen Li verfasserin aut In IEEE Access IEEE, 2014 11(2023), Seite 122516-122525 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:11 year:2023 pages:122516-122525 https://doi.org/10.1109/ACCESS.2023.3329068 kostenfrei https://doaj.org/article/3a8fd74667c9456c99f05d2c16c98fea kostenfrei https://ieeexplore.ieee.org/document/10304136/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 122516-122525
spelling	10.1109/ACCESS.2023.3329068 doi (DE-627)DOAJ098385704 (DE-599)DOAJ3a8fd74667c9456c99f05d2c16c98fea DE-627 ger DE-627 rakwb eng TK1-9971 Shi Luo verfasserin aut Detection and Recognition of Obscured Traffic Signs During Vehicle Movement 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The present study proposes an algorithm to extract obscured traffic sign information from road driving images and fuse it with the vehicle movement process by integrating the vehicle speed. Given the low recognition rate of obscured traffic signs, the proposed algorithm utilizes a traditional color-shape recognition approach to extract potential traffic sign regions. The fusion of traffic sign information from multiple consecutive frames into a single frame enhances the completeness and accuracy of the traffic sign information, bringing it closer to the original characteristics of the traffic sign. The experimental results demonstrate that fusing traffic sign information from the first and third frames improves the template matching similarity by 15.2%. And successfully identifies traffic signs that cannot be recognized by the YOLOV4 and YOLOV8 convolutional neural network when driving at vehicle speeds of <inline-formula< <tex-math notation="LaTeX"<$18\left ({\mathrm {km}/\mathrm {h} }\right)$ </tex-math<</inline-formula<, <inline-formula< <tex-math notation="LaTeX"<$36\left ({\mathrm {km}/\mathrm {h} }\right)$ </tex-math<</inline-formula<, and <inline-formula< <tex-math notation="LaTeX"<$54\left ({\mathrm {km}/\mathrm {h} }\right)$ </tex-math<</inline-formula<. The findings highlight that the proposed algorithm can effectively fuse obscured traffic sign information during vehicle movement to obtain traffic signs with more similar geometric features to the original traffic signs. Traffic sign recognition image recognition image fusion Electrical engineering. Electronics. Nuclear engineering Chenghang Wu verfasserin aut Lingen Li verfasserin aut In IEEE Access IEEE, 2014 11(2023), Seite 122516-122525 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:11 year:2023 pages:122516-122525 https://doi.org/10.1109/ACCESS.2023.3329068 kostenfrei https://doaj.org/article/3a8fd74667c9456c99f05d2c16c98fea kostenfrei https://ieeexplore.ieee.org/document/10304136/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 122516-122525
allfields_unstemmed	10.1109/ACCESS.2023.3329068 doi (DE-627)DOAJ098385704 (DE-599)DOAJ3a8fd74667c9456c99f05d2c16c98fea DE-627 ger DE-627 rakwb eng TK1-9971 Shi Luo verfasserin aut Detection and Recognition of Obscured Traffic Signs During Vehicle Movement 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The present study proposes an algorithm to extract obscured traffic sign information from road driving images and fuse it with the vehicle movement process by integrating the vehicle speed. Given the low recognition rate of obscured traffic signs, the proposed algorithm utilizes a traditional color-shape recognition approach to extract potential traffic sign regions. The fusion of traffic sign information from multiple consecutive frames into a single frame enhances the completeness and accuracy of the traffic sign information, bringing it closer to the original characteristics of the traffic sign. The experimental results demonstrate that fusing traffic sign information from the first and third frames improves the template matching similarity by 15.2%. And successfully identifies traffic signs that cannot be recognized by the YOLOV4 and YOLOV8 convolutional neural network when driving at vehicle speeds of <inline-formula< <tex-math notation="LaTeX"<$18\left ({\mathrm {km}/\mathrm {h} }\right)$ </tex-math<</inline-formula<, <inline-formula< <tex-math notation="LaTeX"<$36\left ({\mathrm {km}/\mathrm {h} }\right)$ </tex-math<</inline-formula<, and <inline-formula< <tex-math notation="LaTeX"<$54\left ({\mathrm {km}/\mathrm {h} }\right)$ </tex-math<</inline-formula<. The findings highlight that the proposed algorithm can effectively fuse obscured traffic sign information during vehicle movement to obtain traffic signs with more similar geometric features to the original traffic signs. Traffic sign recognition image recognition image fusion Electrical engineering. Electronics. Nuclear engineering Chenghang Wu verfasserin aut Lingen Li verfasserin aut In IEEE Access IEEE, 2014 11(2023), Seite 122516-122525 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:11 year:2023 pages:122516-122525 https://doi.org/10.1109/ACCESS.2023.3329068 kostenfrei https://doaj.org/article/3a8fd74667c9456c99f05d2c16c98fea kostenfrei https://ieeexplore.ieee.org/document/10304136/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 122516-122525
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allfieldsSound	10.1109/ACCESS.2023.3329068 doi (DE-627)DOAJ098385704 (DE-599)DOAJ3a8fd74667c9456c99f05d2c16c98fea DE-627 ger DE-627 rakwb eng TK1-9971 Shi Luo verfasserin aut Detection and Recognition of Obscured Traffic Signs During Vehicle Movement 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The present study proposes an algorithm to extract obscured traffic sign information from road driving images and fuse it with the vehicle movement process by integrating the vehicle speed. Given the low recognition rate of obscured traffic signs, the proposed algorithm utilizes a traditional color-shape recognition approach to extract potential traffic sign regions. The fusion of traffic sign information from multiple consecutive frames into a single frame enhances the completeness and accuracy of the traffic sign information, bringing it closer to the original characteristics of the traffic sign. The experimental results demonstrate that fusing traffic sign information from the first and third frames improves the template matching similarity by 15.2%. And successfully identifies traffic signs that cannot be recognized by the YOLOV4 and YOLOV8 convolutional neural network when driving at vehicle speeds of <inline-formula< <tex-math notation="LaTeX"<$18\left ({\mathrm {km}/\mathrm {h} }\right)$ </tex-math<</inline-formula<, <inline-formula< <tex-math notation="LaTeX"<$36\left ({\mathrm {km}/\mathrm {h} }\right)$ </tex-math<</inline-formula<, and <inline-formula< <tex-math notation="LaTeX"<$54\left ({\mathrm {km}/\mathrm {h} }\right)$ </tex-math<</inline-formula<. The findings highlight that the proposed algorithm can effectively fuse obscured traffic sign information during vehicle movement to obtain traffic signs with more similar geometric features to the original traffic signs. Traffic sign recognition image recognition image fusion Electrical engineering. Electronics. Nuclear engineering Chenghang Wu verfasserin aut Lingen Li verfasserin aut In IEEE Access IEEE, 2014 11(2023), Seite 122516-122525 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:11 year:2023 pages:122516-122525 https://doi.org/10.1109/ACCESS.2023.3329068 kostenfrei https://doaj.org/article/3a8fd74667c9456c99f05d2c16c98fea kostenfrei https://ieeexplore.ieee.org/document/10304136/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 122516-122525
language	English
source	In IEEE Access 11(2023), Seite 122516-122525 volume:11 year:2023 pages:122516-122525
sourceStr	In IEEE Access 11(2023), Seite 122516-122525 volume:11 year:2023 pages:122516-122525
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Traffic sign recognition image recognition image fusion Electrical engineering. Electronics. Nuclear engineering
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container_title	IEEE Access
authorswithroles_txt_mv	Shi Luo @@aut@@ Chenghang Wu @@aut@@ Lingen Li @@aut@@
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callnumber-first	T - Technology
author	Shi Luo
spellingShingle	Shi Luo misc TK1-9971 misc Traffic sign recognition misc image recognition misc image fusion misc Electrical engineering. Electronics. Nuclear engineering Detection and Recognition of Obscured Traffic Signs During Vehicle Movement
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topic_title	TK1-9971 Detection and Recognition of Obscured Traffic Signs During Vehicle Movement Traffic sign recognition image recognition image fusion
topic	misc TK1-9971 misc Traffic sign recognition misc image recognition misc image fusion misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Traffic sign recognition misc image recognition misc image fusion misc Electrical engineering. Electronics. Nuclear engineering
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title	Detection and Recognition of Obscured Traffic Signs During Vehicle Movement
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title_full	Detection and Recognition of Obscured Traffic Signs During Vehicle Movement
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doi_str_mv	10.1109/ACCESS.2023.3329068
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title_sort	detection and recognition of obscured traffic signs during vehicle movement
callnumber	TK1-9971
title_auth	Detection and Recognition of Obscured Traffic Signs During Vehicle Movement
abstract	The present study proposes an algorithm to extract obscured traffic sign information from road driving images and fuse it with the vehicle movement process by integrating the vehicle speed. Given the low recognition rate of obscured traffic signs, the proposed algorithm utilizes a traditional color-shape recognition approach to extract potential traffic sign regions. The fusion of traffic sign information from multiple consecutive frames into a single frame enhances the completeness and accuracy of the traffic sign information, bringing it closer to the original characteristics of the traffic sign. The experimental results demonstrate that fusing traffic sign information from the first and third frames improves the template matching similarity by 15.2%. And successfully identifies traffic signs that cannot be recognized by the YOLOV4 and YOLOV8 convolutional neural network when driving at vehicle speeds of <inline-formula< <tex-math notation="LaTeX"<$18\left ({\mathrm {km}/\mathrm {h} }\right)$ </tex-math<</inline-formula<, <inline-formula< <tex-math notation="LaTeX"<$36\left ({\mathrm {km}/\mathrm {h} }\right)$ </tex-math<</inline-formula<, and <inline-formula< <tex-math notation="LaTeX"<$54\left ({\mathrm {km}/\mathrm {h} }\right)$ </tex-math<</inline-formula<. The findings highlight that the proposed algorithm can effectively fuse obscured traffic sign information during vehicle movement to obtain traffic signs with more similar geometric features to the original traffic signs.
abstractGer	The present study proposes an algorithm to extract obscured traffic sign information from road driving images and fuse it with the vehicle movement process by integrating the vehicle speed. Given the low recognition rate of obscured traffic signs, the proposed algorithm utilizes a traditional color-shape recognition approach to extract potential traffic sign regions. The fusion of traffic sign information from multiple consecutive frames into a single frame enhances the completeness and accuracy of the traffic sign information, bringing it closer to the original characteristics of the traffic sign. The experimental results demonstrate that fusing traffic sign information from the first and third frames improves the template matching similarity by 15.2%. And successfully identifies traffic signs that cannot be recognized by the YOLOV4 and YOLOV8 convolutional neural network when driving at vehicle speeds of <inline-formula< <tex-math notation="LaTeX"<$18\left ({\mathrm {km}/\mathrm {h} }\right)$ </tex-math<</inline-formula<, <inline-formula< <tex-math notation="LaTeX"<$36\left ({\mathrm {km}/\mathrm {h} }\right)$ </tex-math<</inline-formula<, and <inline-formula< <tex-math notation="LaTeX"<$54\left ({\mathrm {km}/\mathrm {h} }\right)$ </tex-math<</inline-formula<. The findings highlight that the proposed algorithm can effectively fuse obscured traffic sign information during vehicle movement to obtain traffic signs with more similar geometric features to the original traffic signs.
abstract_unstemmed	The present study proposes an algorithm to extract obscured traffic sign information from road driving images and fuse it with the vehicle movement process by integrating the vehicle speed. Given the low recognition rate of obscured traffic signs, the proposed algorithm utilizes a traditional color-shape recognition approach to extract potential traffic sign regions. The fusion of traffic sign information from multiple consecutive frames into a single frame enhances the completeness and accuracy of the traffic sign information, bringing it closer to the original characteristics of the traffic sign. The experimental results demonstrate that fusing traffic sign information from the first and third frames improves the template matching similarity by 15.2%. And successfully identifies traffic signs that cannot be recognized by the YOLOV4 and YOLOV8 convolutional neural network when driving at vehicle speeds of <inline-formula< <tex-math notation="LaTeX"<$18\left ({\mathrm {km}/\mathrm {h} }\right)$ </tex-math<</inline-formula<, <inline-formula< <tex-math notation="LaTeX"<$36\left ({\mathrm {km}/\mathrm {h} }\right)$ </tex-math<</inline-formula<, and <inline-formula< <tex-math notation="LaTeX"<$54\left ({\mathrm {km}/\mathrm {h} }\right)$ </tex-math<</inline-formula<. The findings highlight that the proposed algorithm can effectively fuse obscured traffic sign information during vehicle movement to obtain traffic signs with more similar geometric features to the original traffic signs.
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title_short	Detection and Recognition of Obscured Traffic Signs During Vehicle Movement
url	https://doi.org/10.1109/ACCESS.2023.3329068 https://doaj.org/article/3a8fd74667c9456c99f05d2c16c98fea https://ieeexplore.ieee.org/document/10304136/ https://doaj.org/toc/2169-3536
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