Cited 13 time in webofscience Cited 18 time in scopus

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

Title
Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture
Authors
Lee, Hyo JongUllah, IhsanWan, WeiguoGao, YongbinFang, Zhijun
Issue Date
2019-02
Citation
Sensors, 19(5)
Type
Article
Article Type
Article
Author Keywords
deep learningresidual SqueezeNetvehicle make recognition
Keywords
REPRESENTATIONSURF
ISSN
1424-8220
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.
URI
http://hdl.handle.net/20.500.11750/9745
DOI
10.3390/s19050982
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Files:
Collection:
ETC1. Journal Articles


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