Research Article
Detection of Potholes on Roads using a Drone
@ARTICLE{10.4108/eai.19-10-2021.171546, author={HemaMalini B.H and Akshay Padesur and Manoj Kumar V and Atish Shet}, title={Detection of Potholes on Roads using a Drone}, journal={EAI Endorsed Transactions on Energy Web}, volume={9}, number={38}, publisher={EAI}, journal_a={EW}, year={2021}, month={10}, keywords={Pothole detection, Drone, Deep Learning, sensing systems, thresholding, YOLOv3, na\~{n}ve-bayes classifier, K-Means}, doi={10.4108/eai.19-10-2021.171546} }- HemaMalini B.H
Akshay Padesur
Manoj Kumar V
Atish Shet
Year: 2021
Detection of Potholes on Roads using a Drone
EW
EAI
DOI: 10.4108/eai.19-10-2021.171546
Abstract
Locating potholes and repairing them is essential, but it has always been a time consuming task for the authorities. This paper presents a way that can help the authorities speed up the pothole detection process by the use of a camera-enabled Unmanned Aerial Vehicle drone. The system is further enabled with a geo-tag and reports the presence of a pothole to the central database which is accessible by the relevant authorities and the common road users. The potholes are located on an open-source map, through which the users using the road can take caution. This increases public safety and helps the concerned authorities take action faster. The model is trained with YOLOv3 algorithm to even detect potholes filled with water, and distinguish potholes from dark road patches, and etc. The results show good accuracy of 85% in detecting the potholes with a low false-negative and false-positive rate.
Copyright © 2021 HemaMalini B.H et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
