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Authors: Qianqian Zhu ; Hang Li and Weiming Guo

Affiliation: Tianjin CATARC Data Co., Ltd., China Automotive Technology & Research Center Co., Ltd., Tianjin, China, China

Keyword(s): Deep learning; Convolutional neural network; Vehicle detection; Direction determination.

Abstract: With the increase of vehicle ownership in China, the number of auto insurance cases is also increasing. The detection and direction determination of vehicles involved in auto insurance cases have important applications in the field of intelligent loss assessment. In this paper, a model of vehicle detection and direction determination based on ResNet-101+FPN backbone network and RetinaNet is built by using convolutional neural network in deep learning. Then, the model is trained and tested on the labelled data set. The model has a relatively high accuracy of prediction, in which the accuracy of vehicle detection reaches 98.7%, and the accuracy of the five directions determination of frontal, lateral-frontal, lateral, lateral-back and back reaches 97.2%.

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Paper citation in several formats:
Zhu, Q.; Li, H. and Guo, W. (2020). Research on Vehicle Detection and Direction Determination based on Deep Learning. In Proceedings of 5th International Conference on Vehicle, Mechanical and Electrical Engineering - ICVMEE; ISBN 978-989-758-412-1; ISSN 2184-741X, SciTePress, pages 26-31. DOI: 10.5220/0008849700260031

@conference{icvmee20,
author={Qianqian Zhu. and Hang Li. and Weiming Guo.},
title={Research on Vehicle Detection and Direction Determination based on Deep Learning},
booktitle={Proceedings of 5th International Conference on Vehicle, Mechanical and Electrical Engineering - ICVMEE},
year={2020},
pages={26-31},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008849700260031},
isbn={978-989-758-412-1},
issn={2184-741X},
}

TY - CONF

JO - Proceedings of 5th International Conference on Vehicle, Mechanical and Electrical Engineering - ICVMEE
TI - Research on Vehicle Detection and Direction Determination based on Deep Learning
SN - 978-989-758-412-1
IS - 2184-741X
AU - Zhu, Q.
AU - Li, H.
AU - Guo, W.
PY - 2020
SP - 26
EP - 31
DO - 10.5220/0008849700260031
PB - SciTePress

- Science and Technology Publications, Lda.
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