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Accurate vehicle type classification serves a significant role in theintelligent transportation system. It is critical for ruler to understand theroad conditions and usually contributive for the traffic light control systemto response correspondingly to alleviate traffic congestion. New technologiesand comprehensive data sources, such as aerial photos and remote sensing data,provide richer and high-dimensional information. Also, due to the rapiddevelopment of deep neural network technology, image based vehicleclassification methods can better extract underlying objective features whenprocessing data. Recently, several deep learning models have been proposed tosolve the problem. However, traditional pure convolutional based approacheshave constraints on global information extraction, and the complex environment,such as bad weather, seriously limits the recognition capability. To improvethe vehicle type classification capability under complex environment, thisstudy proposes a novel Densely Connected Convolutional Transformer inTransformer Neural Network (Dense-TNT) framework for the vehicle typeclassification by stacking Densely Connected Convolutional Network (DenseNet)and Transformer in Transformer (TNT) layers. Three-region vehicle data and fourdifferent weather conditions are deployed for recognition capabilityevaluation. Experimental findings validate the recognition ability of ourproposed vehicle classification model with little decay, even under the heavyfoggy weather condition.

Dense-TNT: Efficient Vehicle Type Classification Neural Network Using Satellite Imagery