ew 18: e4

Research Article

Detection of Potholes on Roads using a Drone

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  • @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: Online First},
        volume={},
        number={},
        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
HemaMalini B.H1,*, Akshay Padesur2, Manoj Kumar V2, Atish Shet2
  • 1: Associate Professor, Dept of CSE, BMS Institute of Technology and Management, Bangalore, India
  • 2: Dept of CSE, BMS Institute of Technology and Management, Bangalore, India
*Contact email: Bhhemaraj@gmail.com

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.