Edge preserving noise robust deep learning networks for vehicle classification (original) (raw)

Concurrency and Computation: Practice and Experience

Abstract

SummaryFor controlling and managing the traffic and to help traffic surveillance, the vehicles classification is a matter of great importance. In the last few decades, vehicle classification systems based on pattern recognition have been utilized to enhance the efficiency for traffic monitoring systems. In the literature many deep learning networks are suggested for vehicle classification. Even though deep learning algorithms are fascinating and growing research area. However, there are several barriers that slow down its progress. The greatest factor that reduces the progress of deep learning systems is the quality of the image. The available vehicle image datasets are affected by noise, weather, and illumination variations. To overcome these issues, we suggest a robust deep learning system by combining bilateral filter individually with three different networks for the improvement of the robustness of vehicle classification in real‐time application. For validation the suggested ne...

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