Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture.
Published in Sensors, 2019
Recommended citation: Ihsan Ullah et al. (2019). Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture; Sensons. 1(1). https://ihsan149.github.io/files/vehicle_type.pdf
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.
Proposed Framework and Examples 