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A 5G Cloud Platform and Machine Learning-Based Mobile Automatic Recognition of Transportation Infrastructure Objects

Ning Chen, Hongyu Shi, Ruijun Liu, Yujie Li, Ji Li, Zijin Xu, Dawei Wang, Guoyang Lu, Baohong Jing, Yue Hou Orcid Logo

IEEE Wireless Communications, Volume: 30, Issue: 2, Pages: 76 - 81

Swansea University Author: Yue Hou Orcid Logo

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DOI (Published version): 10.1109/mwc.002.2200347

Abstract

Crack recognition is important in periodic pavement inspection and maintenance. The wide application of image recognition technology in daily inspection and maintenance makes the health monitoring of asphalt pavement defects more effective, both intelligently and sustainably. In this study, a mobile...

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Published in: IEEE Wireless Communications
ISSN: 1536-1284 1558-0687
Published: Institute of Electrical and Electronics Engineers (IEEE) 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa63288
first_indexed 2023-05-02T08:44:42Z
last_indexed 2024-11-15T18:01:19Z
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In this study, a mobile automatic system integrating fifth-generation wireless communication technology (5G), cloud computing, and artificial intelligence (AI) was proposed for transportation infrastructure object recognition. The original dataset contained 344 images of pavement defects, including longitudinal cracks, transverse cracks, alligator cracks, and broken road markings. Three lightweight algorithms for automatic pavement crack identification were used and compared, including MobileNetV2, ShuffleNetV2, and Res-Net50 networks, respectively. The results showed that the model based on ShuffieNetV2 achieved the best overall predictive accuracy (ACC = 95.52 percent). A mobile automatic monitoring system based on the cloud platform and Android framework was then established. With the help of 5G technology, the cloud-network-terminal&#x2019; interconnection can be achieved to provide fast and stable information transmission between transportation infrastructure and road users. 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spelling 2023-06-09T15:40:32.6296118 v2 63288 2023-05-02 A 5G Cloud Platform and Machine Learning-Based Mobile Automatic Recognition of Transportation Infrastructure Objects 92bf566c65343cb3ee04ad963eacf31b 0000-0002-4334-2620 Yue Hou Yue Hou true false 2023-05-02 ACEM Crack recognition is important in periodic pavement inspection and maintenance. The wide application of image recognition technology in daily inspection and maintenance makes the health monitoring of asphalt pavement defects more effective, both intelligently and sustainably. In this study, a mobile automatic system integrating fifth-generation wireless communication technology (5G), cloud computing, and artificial intelligence (AI) was proposed for transportation infrastructure object recognition. The original dataset contained 344 images of pavement defects, including longitudinal cracks, transverse cracks, alligator cracks, and broken road markings. Three lightweight algorithms for automatic pavement crack identification were used and compared, including MobileNetV2, ShuffleNetV2, and Res-Net50 networks, respectively. The results showed that the model based on ShuffieNetV2 achieved the best overall predictive accuracy (ACC = 95.52 percent). A mobile automatic monitoring system based on the cloud platform and Android framework was then established. With the help of 5G technology, the cloud-network-terminal’ interconnection can be achieved to provide fast and stable information transmission between transportation infrastructure and road users. The proposed system provides an engineering reference for the transportation infrastructure inspection and maintenance using the 5G communication technology. Journal Article IEEE Wireless Communications 30 2 76 81 Institute of Electrical and Electronics Engineers (IEEE) 1536-1284 1558-0687 18 4 2023 2023-04-18 10.1109/mwc.002.2200347 http://dx.doi.org/10.1109/mwc.002.2200347 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University This work was supported by Key Science and Technology Projects in the Transportation Industry in 2021 (2021-ZD2-047), Plan Project of Shandong Transportation S&T (2021B49), Natural Science Foundation of Heilongjiang Province of China (JJ2020ZD0015), and Opening Project Fund of Materials Service Safety Assessment Facilities (MSAF-2021-005). The authors would like to express sincere gratitude to Prof. Xingyu Gu for sharing the data. 2023-06-09T15:40:32.6296118 2023-05-02T09:39:04.2785764 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Ning Chen 1 Hongyu Shi 2 Ruijun Liu 3 Yujie Li 4 Ji Li 5 Zijin Xu 6 Dawei Wang 7 Guoyang Lu 8 Baohong Jing 9 Yue Hou 0000-0002-4334-2620 10 63288__27294__29c0c00d035e453286cc6c90575766e7.pdf 63288.pdf 2023-05-02T14:29:01.0660357 Output 510475 application/pdf Accepted Manuscript true false
title A 5G Cloud Platform and Machine Learning-Based Mobile Automatic Recognition of Transportation Infrastructure Objects
spellingShingle A 5G Cloud Platform and Machine Learning-Based Mobile Automatic Recognition of Transportation Infrastructure Objects
Yue Hou
title_short A 5G Cloud Platform and Machine Learning-Based Mobile Automatic Recognition of Transportation Infrastructure Objects
title_full A 5G Cloud Platform and Machine Learning-Based Mobile Automatic Recognition of Transportation Infrastructure Objects
title_fullStr A 5G Cloud Platform and Machine Learning-Based Mobile Automatic Recognition of Transportation Infrastructure Objects
title_full_unstemmed A 5G Cloud Platform and Machine Learning-Based Mobile Automatic Recognition of Transportation Infrastructure Objects
title_sort A 5G Cloud Platform and Machine Learning-Based Mobile Automatic Recognition of Transportation Infrastructure Objects
author_id_str_mv 92bf566c65343cb3ee04ad963eacf31b
author_id_fullname_str_mv 92bf566c65343cb3ee04ad963eacf31b_***_Yue Hou
author Yue Hou
author2 Ning Chen
Hongyu Shi
Ruijun Liu
Yujie Li
Ji Li
Zijin Xu
Dawei Wang
Guoyang Lu
Baohong Jing
Yue Hou
format Journal article
container_title IEEE Wireless Communications
container_volume 30
container_issue 2
container_start_page 76
publishDate 2023
institution Swansea University
issn 1536-1284
1558-0687
doi_str_mv 10.1109/mwc.002.2200347
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
url http://dx.doi.org/10.1109/mwc.002.2200347
document_store_str 1
active_str 0
description Crack recognition is important in periodic pavement inspection and maintenance. The wide application of image recognition technology in daily inspection and maintenance makes the health monitoring of asphalt pavement defects more effective, both intelligently and sustainably. In this study, a mobile automatic system integrating fifth-generation wireless communication technology (5G), cloud computing, and artificial intelligence (AI) was proposed for transportation infrastructure object recognition. The original dataset contained 344 images of pavement defects, including longitudinal cracks, transverse cracks, alligator cracks, and broken road markings. Three lightweight algorithms for automatic pavement crack identification were used and compared, including MobileNetV2, ShuffleNetV2, and Res-Net50 networks, respectively. The results showed that the model based on ShuffieNetV2 achieved the best overall predictive accuracy (ACC = 95.52 percent). A mobile automatic monitoring system based on the cloud platform and Android framework was then established. With the help of 5G technology, the cloud-network-terminal’ interconnection can be achieved to provide fast and stable information transmission between transportation infrastructure and road users. The proposed system provides an engineering reference for the transportation infrastructure inspection and maintenance using the 5G communication technology.
published_date 2023-04-18T08:21:13Z
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score 11.067666

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