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Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture
- Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture
- Lee, Hyo Jong; Ullah, Ihsan; Wan, Weiguo; Gao, Yongbin; Fang, Zhijun
- Issue Date
- Sensors, 19(5)
- Article Type
- Author Keywords
- deep learning; residual SqueezeNet; vehicle make recognition
- REPRESENTATION; SURF
- 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.
- Multidisciplinary Digital Publishing Institute (MDPI)
- ETC1. Journal Articles
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