Ford vehicle identification via shallow neural network trained by particle swarm optimization

DOI

10.2991/jimec-18.2018.18How to use a DOI?

Keywords

Ford vehicle; identification; wavelet entropy; shallow neural network; particle swarm optimization; cross validation

Abstract

Automatic identification of the car manufacturer is difficult to achieve because of the similarity among the different brands. In this work, we propose a new system of Ford vehicle identification. Firstly, we captured the side view of the car image. Secondly, we employed the wavelet entropy (WE) to extract efficient features from car images. Thirdly, we employed a shallow neural network (SNN) as a classifier. Finally, we used the particle swarm optimization to train the classifier. The 10 10 - fold cross validation on a data set containing 220 vehicle images showed that our Ford vehicle identification system obtained the overall sensitivity of 83.27±1.61%. The overall specificity is 83.91± 1.87%, the overall accuracy is 83.59± 0.94%. Experiment result show that the proposed system is effective for Ford vehicle identification.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jingyuan Yang
AU  - Lei Wang
AU  - Qiaoyong Jiang
PY  - 2018/12
DA  - 2018/12
TI  - Ford vehicle identification via shallow neural network trained by particle swarm optimization
BT  - Proceedings of the 2018 3rd Joint International Information Technology，Mechanical and Electronic Engineering Conference (JIMEC 2018)
PB  - Atlantis Press
SP  - 84
EP  - 88
SN  - 2589-4943
UR  - https://doi.org/10.2991/jimec-18.2018.18
DO  - 10.2991/jimec-18.2018.18
ID  - Yang2018/12
ER  -