@inproceedings{d7bd4e999fe74f29807cff794c604dec,
title = "Improving road damage maintenance in South Africa using deep learning",
abstract = "The preservation of the road infrastructure is one of the essential factors for a safe, economical and sustainable transport system. Manually collecting data is tedious. This field is intended to benefit from the advancement of artificial intelligence technologies. Advances in deep learning enable the automatic detection of road damage from the collected road images. This work proposes to use an Indian subset of the Road Damage Dataset (RDD) 2022, which has a plethora of images of streets worldwide. The data is processed and labelled. Then, a YOLOv5s model is trained and validated. The model is evaluated against 1959 test images, and the results are tabulated and discussed. The proposed approach has the F1 results of 41% for road damage data collected from the RDD 2022 India subset. In the future, it is recommended to use the broader Global RDD 2022 data to train more robust models with higher accuracy.",
keywords = "Machine learning, RDD2022_India, Road Damage Detection, Road Infrastructure, sustainable transport, YOLO5",
author = "Devesh Mothilall and {Van Zyl}, Terence",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2024 ; Conference date: 01-02-2024 Through 02-02-2024",
year = "2024",
doi = "10.1109/ACDSA59508.2024.10467363",
language = "English",
series = "International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2024",
address = "United States",
}
