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RCD-IIUM: a comprehensive Malaysian road crack dataset for infrastructure analysis

In rapidly urbanizing regions, maintaining road infrastructure integrity is a critical challenge due to increasing vehicular stress and environmental factors. This study introduces the Road Crack Dataset-International Islamic University Malaysia (RCD-IIUM), designed to enhance road pavement inf...

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Main Authors: Ashraf, Arselan, Sophian, Ali, Shafie, Amir Akramin, Gunawan, Teddy Surya, Ismail, Norfarah Nadia, Bawono, Ali Aryo
Format: Proceeding Paper
Language:English
Published: IEEE Xplore 2024
Subjects:
T Technology (General)
Online Access:http://irep.iium.edu.my/114395/7/114395_%20RCD-IIUM%20a%20comprehensive.pdf
http://irep.iium.edu.my/114395/
https://ieeexplore.ieee.org/document/10652339
https://doi.org/10.1109/ICOM61675.2024.10652339
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spelling my.iium.irep.1143952024-09-11T08:53:13Z http://irep.iium.edu.my/114395/ RCD-IIUM: a comprehensive Malaysian road crack dataset for infrastructure analysis Ashraf, Arselan Sophian, Ali Shafie, Amir Akramin Gunawan, Teddy Surya Ismail, Norfarah Nadia Bawono, Ali Aryo T Technology (General) In rapidly urbanizing regions, maintaining road infrastructure integrity is a critical challenge due to increasing vehicular stress and environmental factors. This study introduces the Road Crack Dataset-International Islamic University Malaysia (RCD-IIUM), designed to enhance road pavement infrastructure management in Malaysia. Employing advanced data collection technologies, including high resolution digital imaging, the dataset captures detailed anomalies in road surfaces, laying the groundwork for robust infrastructure analysis. The utility and efficacy of the RCDIIUM dataset were evaluated through the deployment of three deep learning models: Customized YOLOv7, YOLOv8X-SEG, and an Advanced Hybrid Deep Learning Model. These models were tested for their ability to detect and classify road cracks using metrics such as precision, recall, F1-score, and overall accuracy. Results indicated that the YOLOv8X-SEG model outperformed others, demonstrating higher accuracy of 90% and F1-score of 95%. The Customized YOLOv7 model achieved a precision of 93%, recall of 91.58%, and overall accuracy of 88%. The Advanced Hybrid Deep Learning Model achieved a precision of 88%, recall of 89%, F1-score of 88.5%, and overall accuracy of 85%, further validating the robustness of the dataset. The dataset not only bolsters road pavement maintenance strategies but also supports data-driven decision making for urban planning and policymaking. IEEE Xplore 2024-09-04 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/114395/7/114395_%20RCD-IIUM%20a%20comprehensive.pdf Ashraf, Arselan and Sophian, Ali and Shafie, Amir Akramin and Gunawan, Teddy Surya and Ismail, Norfarah Nadia and Bawono, Ali Aryo (2024) RCD-IIUM: a comprehensive Malaysian road crack dataset for infrastructure analysis. In: 9th International Conference on Mechatronics Engineering, 13-14 August 2024, Kulliyyah of Engineering, IIUM. https://ieeexplore.ieee.org/document/10652339 https://doi.org/10.1109/ICOM61675.2024.10652339
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Ashraf, Arselan
Sophian, Ali
Shafie, Amir Akramin
Gunawan, Teddy Surya
Ismail, Norfarah Nadia
Bawono, Ali Aryo
RCD-IIUM: a comprehensive Malaysian road crack dataset for infrastructure analysis
description In rapidly urbanizing regions, maintaining road infrastructure integrity is a critical challenge due to increasing vehicular stress and environmental factors. This study introduces the Road Crack Dataset-International Islamic University Malaysia (RCD-IIUM), designed to enhance road pavement infrastructure management in Malaysia. Employing advanced data collection technologies, including high resolution digital imaging, the dataset captures detailed anomalies in road surfaces, laying the groundwork for robust infrastructure analysis. The utility and efficacy of the RCDIIUM dataset were evaluated through the deployment of three deep learning models: Customized YOLOv7, YOLOv8X-SEG, and an Advanced Hybrid Deep Learning Model. These models were tested for their ability to detect and classify road cracks using metrics such as precision, recall, F1-score, and overall accuracy. Results indicated that the YOLOv8X-SEG model outperformed others, demonstrating higher accuracy of 90% and F1-score of 95%. The Customized YOLOv7 model achieved a precision of 93%, recall of 91.58%, and overall accuracy of 88%. The Advanced Hybrid Deep Learning Model achieved a precision of 88%, recall of 89%, F1-score of 88.5%, and overall accuracy of 85%, further validating the robustness of the dataset. The dataset not only bolsters road pavement maintenance strategies but also supports data-driven decision making for urban planning and policymaking.
format Proceeding Paper
author Ashraf, Arselan
Sophian, Ali
Shafie, Amir Akramin
Gunawan, Teddy Surya
Ismail, Norfarah Nadia
Bawono, Ali Aryo
author_facet Ashraf, Arselan
Sophian, Ali
Shafie, Amir Akramin
Gunawan, Teddy Surya
Ismail, Norfarah Nadia
Bawono, Ali Aryo
author_sort Ashraf, Arselan
title RCD-IIUM: a comprehensive Malaysian road crack dataset for infrastructure analysis
title_short RCD-IIUM: a comprehensive Malaysian road crack dataset for infrastructure analysis
title_full RCD-IIUM: a comprehensive Malaysian road crack dataset for infrastructure analysis
title_fullStr RCD-IIUM: a comprehensive Malaysian road crack dataset for infrastructure analysis
title_full_unstemmed RCD-IIUM: a comprehensive Malaysian road crack dataset for infrastructure analysis
title_sort rcd-iium: a comprehensive malaysian road crack dataset for infrastructure analysis
publisher IEEE Xplore
publishDate 2024
url http://irep.iium.edu.my/114395/7/114395_%20RCD-IIUM%20a%20comprehensive.pdf
http://irep.iium.edu.my/114395/
https://ieeexplore.ieee.org/document/10652339
https://doi.org/10.1109/ICOM61675.2024.10652339
_version_ 1811679635621019648
score 13.244413

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