Pavement Distress Detection Using Artificial Intelligence Algorithm

DOI

10.2991/978-94-6463-678-9_24How to use a DOI?

Keywords

Pavement Distress Detection; Artificial Intelligence; Machine Learning; Computer Vision; Infrastructure Management

Abstract

Road infrastructure is a crucial component of modern transportation systems, and its maintenance is essential to ensure safety and efficiency. To avert road degradation from undermining safety and functionality, it is essential to detect road damage promptly. Pavement distress detection is a time-consuming and labor-intensive process that requires manual visual inspection. This study proposes the development of an artificial intelligence (AI) using YOLOVv8 algorithm for automated pavement distress detection. The proposed algorithm utilizes machine learning and computer vision techniques to analyze images of pavement surfaces and identify various types of distresses, including cracks, potholes, and roughness. The algorithm is trained on a large dataset of labeled images and evaluated using performance metrics such as accuracy, precision, and recall. The results show that the proposed algorithm achieves high accuracy and outperforms traditional manual inspection methods. The use of AI in pavement distress detection can significantly reduce maintenance costs, improve road safety, and enhance the overall efficiency of transportation infrastructure. This study contributes to the development of smart and sustainable infrastructure management systems.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Mahmuda
AU  - Didi Yuda Wiranata
AU  - Revias
AU  - Lino Garda Denaro
AU  - Gamma Ade Pertiwi
AU  - M. Farizki Budiman
AU  - Zaki AlHusin Basami
AU  - Nabila Rara Sapitri
PY  - 2025
DA  - 2025/05/01
TI  - Pavement Distress Detection Using Artificial Intelligence Algorithm
BT  - Proceedings of the 8th FIRST 2024 International Conference on Global Innovations (FIRST-ESCSI 2024 )
PB  - Atlantis Press
SP  - 256
EP  - 264
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-678-9_24
DO  - 10.2991/978-94-6463-678-9_24
ID  - 2025
ER  -