2019
DOI: 10.3390/s19050982
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Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture

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 resu… Show more

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Cited by 94 publications
(41 citation statements)
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“…Recognition Rate ( RR ): It is the ratio of the wrongly recognized image/the recognized image [ 27 ].…”
Section: Methodsmentioning
confidence: 99%
“…Recognition Rate ( RR ): It is the ratio of the wrongly recognized image/the recognized image [ 27 ].…”
Section: Methodsmentioning
confidence: 99%
“…In the Compression part, the depth of the input image is reduced and then increased (bottleneck). In the Expansion part, the depth is increased [21,22]. Table 3 presents the layers and default parameter values of the model, these values are used without changes.…”
Section: Deep Learning Model: Squeezenetmentioning
confidence: 99%
“…The performance of [31,32] is sufficient for VMMR work but the authors do not discuss the computational cost in their work. The performance and computational cost of [40,41] are sufficient for VMMR work but less than what has been presented here. The work proposed in [42] does not provides satisfactory results on the NTOU-MMR dataset.…”
Section: Comparison With State-of-the-artmentioning
confidence: 99%