Automatic vehicle classification using center strengthened convolutional neural network
Kuan-Chung Wang, Yoga Dwi Pranata, Jia‐Ching Wang · 2017
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.