  Accurate vehicle type classification serves a significant role in the
intelligent transportation system. It is critical for ruler to understand the
road conditions and usually contributive for the traffic light control system
to response correspondingly to alleviate traffic congestion. New technologies
and comprehensive data sources, such as aerial photos and remote sensing data,
provide richer and high-dimensional information. Also, due to the rapid
development of deep neural network technology, image based vehicle
classification methods can better extract underlying objective features when
processing data. Recently, several deep learning models have been proposed to
solve the problem. However, traditional pure convolutional based approaches
have constraints on global information extraction, and the complex environment,
such as bad weather, seriously limits the recognition capability. To improve
the vehicle type classification capability under complex environment, this
study proposes a novel Densely Connected Convolutional Transformer in
Transformer Neural Network (Dense-TNT) framework for the vehicle type
classification by stacking Densely Connected Convolutional Network (DenseNet)
and Transformer in Transformer (TNT) layers. Three-region vehicle data and four
different weather conditions are deployed for recognition capability
evaluation. Experimental findings validate the recognition ability of our
proposed vehicle classification model with little decay, even under the heavy
foggy weather condition.
