Autonomous Road Potholes Detection on Video


Ho, Chiung Ching and Yap, Timothy Tzen Vun and Ng, Hu and Tong, Hau Lee and Goh, Vik Tor and Koh, Jia Juang and Kuek, Thiam Yong (2019) Autonomous Road Potholes Detection on Video. In: Computational Science and Technology. Springer, Lecture Notes in Electrical Engineering, pp. 137-143. ISBN 978-981131055-3

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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.

Item Type: Book Section
Uncontrolled Keywords: Machine Learning
Subjects: Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions: Faculty of Computing and Informatics (FCI)
Faculty of Engineering (FOE)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 26 Jan 2022 02:32
Last Modified: 26 Jan 2022 02:34


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