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Detection and Statistics System of Pavement Distresses Based on Street View Videos

Zhiyuan Zhang Orcid Logo, Fang Liu Orcid Logo, Yucheng Huang Orcid Logo, Yue Hou Orcid Logo

IEEE Transactions on Intelligent Transportation Systems, Volume: 25, Issue: 10, Pages: 15106 - 15115

Swansea University Author: Yue Hou Orcid Logo

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DOI (Published version): 10.1109/tits.2024.3401150

Abstract

Timely detection and statistical analysis of pavement distresses are essential for improving road maintenance efficiency. However, traditional methods for pavement defect detection face challenges such as inefficiency and high equipment costs. In response to these challenges, this paper proposes a p...

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Published in: IEEE Transactions on Intelligent Transportation Systems
ISSN: 1524-9050 1558-0016
Published: Institute of Electrical and Electronics Engineers (IEEE) 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa67684
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spelling v2 67684 2024-09-13 Detection and Statistics System of Pavement Distresses Based on Street View Videos 92bf566c65343cb3ee04ad963eacf31b 0000-0002-4334-2620 Yue Hou Yue Hou true false 2024-09-13 ACEM Timely detection and statistical analysis of pavement distresses are essential for improving road maintenance efficiency. However, traditional methods for pavement defect detection face challenges such as inefficiency and high equipment costs. In response to these challenges, this paper proposes a pavement defect detection and statistical system based on street view videos. Initially, we introduce an enhanced algorithm named SN-YOLO (Slim-neck YOLO) designed to address the issue of low model detection accuracy in complex background environments meanwhile achieve model lightweighting. Specifically, the GSConv lightweight convolution module is employed to minimize the model size, while the VoVGSCSP and VoVGSCSP-cheap modules are incorporated to augment the original C2f module, thereby refining the model’s recognition capabilities in intricate backgrounds. Moreover, by incorporating Soft-NMS for post-processing optimization, the model’s robustness in detecting multi-scale defects is enhanced. Experimental results on the open-source dataset RDD2022 and a proprietary dataset demonstrate that the improved SN-YOLO algorithm surpasses current state-of-the-art methods. Furthermore, by leveraging the SN-YOLO algorithm and the Deep oc-sort tracking algorithm, we develop a deployable pavement distress detection and statistic system. In the application to real-world road street view video analysis, the system exhibits unparalleled accuracy and efficiency in defect detection and data compilation, presenting a robust solution for expedited, large-scale assessment of pavement conditions. Journal Article IEEE Transactions on Intelligent Transportation Systems 25 10 15106 15115 Institute of Electrical and Electronics Engineers (IEEE) 1524-9050 1558-0016 4 10 2024 2024-10-04 10.1109/tits.2024.3401150 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 52208360) Natural Science Foundation of Jiangsu Province, China (Grant Number: BK20210720) Jiangsu Geology and Mineral Exploration Bureau (Grant Number: 2021KY06) 2024-10-24T14:07:14.2060127 2024-09-13T11:40:00.1865524 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Zhiyuan Zhang 0009-0001-6047-5513 1 Fang Liu 0009-0001-5892-990x 2 Yucheng Huang 0000-0003-1728-2246 3 Yue Hou 0000-0002-4334-2620 4
title Detection and Statistics System of Pavement Distresses Based on Street View Videos
spellingShingle Detection and Statistics System of Pavement Distresses Based on Street View Videos
Yue Hou
title_short Detection and Statistics System of Pavement Distresses Based on Street View Videos
title_full Detection and Statistics System of Pavement Distresses Based on Street View Videos
title_fullStr Detection and Statistics System of Pavement Distresses Based on Street View Videos
title_full_unstemmed Detection and Statistics System of Pavement Distresses Based on Street View Videos
title_sort Detection and Statistics System of Pavement Distresses Based on Street View Videos
author_id_str_mv 92bf566c65343cb3ee04ad963eacf31b
author_id_fullname_str_mv 92bf566c65343cb3ee04ad963eacf31b_***_Yue Hou
author Yue Hou
author2 Zhiyuan Zhang
Fang Liu
Yucheng Huang
Yue Hou
format Journal article
container_title IEEE Transactions on Intelligent Transportation Systems
container_volume 25
container_issue 10
container_start_page 15106
publishDate 2024
institution Swansea University
issn 1524-9050
1558-0016
doi_str_mv 10.1109/tits.2024.3401150
publisher Institute of Electrical and Electronics Engineers (IEEE)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
document_store_str 0
active_str 0
description Timely detection and statistical analysis of pavement distresses are essential for improving road maintenance efficiency. However, traditional methods for pavement defect detection face challenges such as inefficiency and high equipment costs. In response to these challenges, this paper proposes a pavement defect detection and statistical system based on street view videos. Initially, we introduce an enhanced algorithm named SN-YOLO (Slim-neck YOLO) designed to address the issue of low model detection accuracy in complex background environments meanwhile achieve model lightweighting. Specifically, the GSConv lightweight convolution module is employed to minimize the model size, while the VoVGSCSP and VoVGSCSP-cheap modules are incorporated to augment the original C2f module, thereby refining the model’s recognition capabilities in intricate backgrounds. Moreover, by incorporating Soft-NMS for post-processing optimization, the model’s robustness in detecting multi-scale defects is enhanced. Experimental results on the open-source dataset RDD2022 and a proprietary dataset demonstrate that the improved SN-YOLO algorithm surpasses current state-of-the-art methods. Furthermore, by leveraging the SN-YOLO algorithm and the Deep oc-sort tracking algorithm, we develop a deployable pavement distress detection and statistic system. In the application to real-world road street view video analysis, the system exhibits unparalleled accuracy and efficiency in defect detection and data compilation, presenting a robust solution for expedited, large-scale assessment of pavement conditions.
published_date 2024-10-04T14:07:12Z
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score 11.036706

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