Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture

Sensors
Hyo Jong LeeZhijun Fang

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.

References

Jul 29, 2006·Science·G E Hinton, R R Salakhutdinov
Feb 16, 2016·Sensors·Yongbin Gao, Hyo Jong Lee

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Citations

Feb 23, 2020·Sensors·Adeel Ahmad JamilSergio A Velastin

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Software Mentioned

GoogLeNet
Fire9
DIGITS
AdaBoost
Caffe
SqueezeNet
Faster CNN
AlexNet
Cuda
YOLO

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