@inproceedings{7814301a3daa46b1ab214d2911ebb735,
title = "Automatic vehicle classification using center strengthened convolutional neural network",
abstract = "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.",
keywords = "Convolutional Neural Network, Deep learning, ROI pooling, Vehicle classification",
author = "Wang, {Kuan Chung} and Pranata, {Yoga Dwi} and Wang, {Jia Ching}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017 ; Conference date: 12-12-2017 Through 15-12-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/APSIPA.2017.8282187",
language = "???core.languages.en_GB???",
series = "Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1075--1078",
booktitle = "Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017",
}