@inproceedings{c1f4d23e903148cdb2dad7d7cf237260,
title = "Autonomous road potholes detection on video",
abstract = "This research work explores the possibility of using deep learning to produce an autonomous system for detecting potholes on video to assist in road monitoring and maintenance. Video data of roads was collected using a GoPro camera mounted on a car. Region-based Fully Convolutional Networks (RFCN) was employed to produce the model to detect potholes from images, and validated on the collected videos. The R-FCN model is able to achieve a Mean Average Precision (MAP) of 89% and a True Positive Rate (TPR) of 89% with no false positive.",
keywords = "Deep learning, Machine learning, Object identification, Road surface defects, Video data",
author = "Koh, {Jia Juang} and Yap, {Timothy Tzen Vun} and Hu Ng and Goh, {Vik Tor} and Tong, {Hau Lee} and Ho, {Chiung Ching} and Kuek, {Thiam Yong}",
note = "Funding Information: Acknowledgements. Financial support from the Ministry of Higher Education, Malaysia, under the Fundamental Research Grant Scheme with grant number FRGS/1/2015/SG07/MMU/02/1, as well as the Multimedia University Capex Fund with Project ID MMUI/CAPEX170008, are gratefully acknowledged. Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2019.; 5th International Conference on Computational Science and Technology 2018, ICCST 2018 ; Conference date: 29-08-2018 Through 30-08-2018",
year = "2019",
doi = "10.1007/978-981-13-2622-6_14",
language = "English",
isbn = "9789811326219",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer",
pages = "137--143",
editor = "Rayner Alfred and Ibrahim, {Ag Asri Ag} and Yuto Lim and Patricia Anthony",
booktitle = "Computational Science and Technology",
}