Automatic vehicle classification using center strengthened convolutional neural network

Vehicle classification is one of the major part for the smart road management system and traffic management system. The use of appropriate algorithms has a significant impact in the process of classification. In this paper, we propose a deep neural network, named center strengthened convolutional neural network (CS-CNN), for handling central part image feature enhancement with non-fixed size input. The main hallmark of this proposed architecture is center enhancement that extract additional feature from central of image by ROI pooling. Another, our CS-CNN, based on VGG network architecture, joint with ROI pooling layer to get elaborate feature maps. Our proposed method will be compared with other typical deep learning architecture like VGG-s and VGG-Verydeep-16. In the experiments, we show the outstanding performance which getting more than 97% accuracy on vehicle classification with only few training data from Caltech256 datasets.