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

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 5; p. 982
• Main Authors: Lee, Hyo Jong, Ullah, Ihsan, Wan, Weiguo, Gao, Yongbin, Fang, Zhijun
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 26.02.2019; MDPI AG
• Subjects: deep learning; residual SqueezeNet; vehicle make recognition; deep learning; residual SqueezeNet; vehicle make recognition
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19050982

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.