Citation

Lee HJ, Ullah I, Wan W, Gao Y, Fang Z. Sensors (Basel) 2019; 19(5): s19050982.

Affiliation

School of Electrics and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China. zjfang@foxmail.com.

Copyright

(Copyright © 2019, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s19050982

PMID

30813512

Abstract

Make and model recognition (MMR) of vehicles plays an important role in automatic vision-based systems. This paper proposes a novel deep learning approach for MMR using the SqueezeNet architecture. The frontal views of vehicle images are first extracted and fed into a deep network for training and testing. The SqueezeNet architecture with bypass connections between the Fire modules, a variant of the vanilla SqueezeNet, is employed for this study, which makes our MMR system more efficient. The experimental results on our collected large-scale vehicle datasets indicate that the proposed model achieves 96.3% recognition rate at the rank-1 level with an economical time slice of 108.8 ms. For inference tasks, the deployed deep model requires less than 5 MB of space and thus has a great viability in real-time applications.

Language: en

Keywords

deep learning; residual SqueezeNet; vehicle make recognition