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Authors: Burak Balci ; Yusuf Artan ; Bensu Alkan and Alperen Elihos

Affiliation: Video Analysis Group, HAVELSAN Incorporation, Ankara and Turkey

Keyword(s): Intelligent Transportation Systems (ITS), Deep Learning, Object Detection, Image Classification.

Abstract: Vehicle body damage detection from still images has received considerable interest in the computer vision community in recent years. Existing methods are typically developed towards the auto insurance industry to minimize the claim leakage problem. Earlier studies utilized images taken from short proximity (< 3 meters) to the vehicle or to the damaged region of vehicle. In this study, we investigate the vehicle frontal body damage detection using roadway surveillance camera images. The proposed method utilizes deep learning based object detection and image classification methods to determine damage status of a vehicle. The proposed method combines the symmetry property of vehicles’ frontal view and transfer learning concept in its inference process. Experimental results show that the proposed method achieves 91 % accuracy on a test dataset.

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Paper citation in several formats:
Balci, B.; Artan, Y.; Alkan, B. and Elihos, A. (2019). Front-View Vehicle Damage Detection using Roadway Surveillance Camera Images . In Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS; ISBN 978-989-758-374-2; ISSN 2184-495X, SciTePress, pages 193-198. DOI: 10.5220/0007724601930198

@conference{vehits19,
author={Burak Balci. and Yusuf Artan. and Bensu Alkan. and Alperen Elihos.},
title={Front-View Vehicle Damage Detection using Roadway Surveillance Camera Images },
booktitle={Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS},
year={2019},
pages={193-198},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007724601930198},
isbn={978-989-758-374-2},
issn={2184-495X},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS
TI - Front-View Vehicle Damage Detection using Roadway Surveillance Camera Images
SN - 978-989-758-374-2
IS - 2184-495X
AU - Balci, B.
AU - Artan, Y.
AU - Alkan, B.
AU - Elihos, A.
PY - 2019
SP - 193
EP - 198
DO - 10.5220/0007724601930198
PB - SciTePress

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