TY  - JOUR
PY  - 2019//
TI  - Real-time vehicle make and model recognition with the residual SqueezeNet architecture
JO  - Sensors (Basel)
A1  - Lee, Hyo Jong
A1  - Ullah, Ihsan
A1  - Wan, Weiguo
A1  - Gao, Yongbin
A1  - Fang, Zhijun
SP  - s19050982
EP  - s19050982
VL  - 19
IS  - 5
N2  - 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>
LA  - en
SN  - 1424-8220
UR  - http://dx.doi.org/10.3390/s19050982
ID  - ref1
ER  -