@article{ref1,
title="Real-time vehicle make and model recognition with the residual SqueezeNet architecture",
journal="Sensors (Basel)",
year="2019",
author="Lee, Hyo Jong and Ullah, Ihsan and Wan, Weiguo and Gao, Yongbin and Fang, Zhijun",
volume="19",
number="5",
pages="s19050982-s19050982",
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.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s19050982",
url="http://dx.doi.org/10.3390/s19050982"
}