Automatic Vehicle Classification using Center Strengthened Convolutional Neural Network Kuan-Chung Wang, Yoga Dwi Pranata, and Jia-Ching Wang Department of Computer Science and Information Engineering, National Central University, Taiwan 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- Deep learning, Convolutional Neural Network, ROI pooling, Vehicle classification I. INTRODUCTION Nowadays, motorists rarely pay attention to the traffic signs that exist. Motorists make shortcuts to get to the destination quickly. But it can unconsciously cause some harm to both the driver of the vehicle itself and others. Whereas every rider of the vehicle already knows the rules on the road but ignore it. As transportation system has become increasingly intelligent with the rapid increase of traffic demand in these years, applying Intelligent Transportation System (ITS) technology becomes one of the fundamental measures to make use of the existing transportation infrastructures reasonably and scientifically. Meanwhile, vehicle detection and classification technology is an important component of Intelligent Transportation System, which provides initial and necessary information of the traffic for Intelligent Transportation System. Up to date, there have been numbers of the proposed approaches to discuss the problem of vehicle classification [1]–[5]. Most of the proposed approaches can be seen as sensor-based and visual-based approach. The sensor-based approach needs some particular sensor installation in the road networks. This method seems easy to implement but we need consider some factor likes high cost, less flexibility in the system, and the weather forecast. Generally, by using some sensors installed on the road networks (e.g. magnetic sensor [1], piezoelectric sensor [2], Traffic Inductive sensors[3], infrared transceivers, or other sensor devices), these methods obtain relevant physical parameters of the vehicles such as the width, height, and the number of tiers, and then use that information to directly classify the vehicles type. In the visual-based approach, it needs some visual appearance of the vehicle to the system to classify the vehicle. The advantages of the visual-based are low-cost and have a high accuracy to classify the vehicle. Visual information about the vehicle can be represented that computer can identify the image, then the type of vehicles can be obtained by running a particular classification algorithm. Surendra et al[4] proposed a vision-based vehicle classification using segmentation and blob-tracking. Andrew et al.[5] classify the vehicle using a rectangle in the images and estimate the dimension of the vehicle. Jun and Yong[6] proposed two steps of vehicle classification that is inter-class vehicle classification and intra-class vehicle classification. For some algorithm, there are some limitations for the visual-based approach to classifying the vehicle. Canny algorithm there are limitations that cannot recognize the vehicle when night comes or in the dark with a long response time, while for the algorithm Robert and Prewitt very bad in recognizing the moving object. In recent years, deep learning method have many successes in the areas of classification such as speech and image. Specially, convolutional neural network base method performs state-of-the-art on many image classification task. Since from 2012 on ImageNet Large Scale Visual Recognition Competition (ILSVR), there were many typical architectures coming out [6]-[8]. In view of data-driven learning, deep learning also make problem more easy than designing an algorithm by ourselves. Based on these reasons, our proposal depends on deep learning method and develop CS-CNN. In this paper, we proposed an end-to-end Convolutional Neural Network to classify the vehicle based on VGG net. We use VGG network architecture as the pre-trained model. We combine ROI pooling from SPP [12] network and develop center strengthened net. Before fully connected layer, we spread the feature maps into two, the first one is to get the feature from the images and the second is to get the feature Proceedings of APSIPA Annual Summit and Conference 2017 12 - 15 December 2017, Malaysia 978-1-5386-1542-3@2017 APSIPA APSIPA ASC 2017
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Automatic Vehicle Classification using Center
Strengthened Convolutional Neural Network Kuan-Chung Wang, Yoga Dwi Pranata, and Jia-Ching Wang
Department of Computer Science and Information Engineering, National Central University, Taiwan
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- Deep learning, Convolutional Neural
Network, ROI pooling, Vehicle classification
I. INTRODUCTION
Nowadays, motorists rarely pay attention to the traffic
signs that exist. Motorists make shortcuts to get to the
destination quickly. But it can unconsciously cause some
harm to both the driver of the vehicle itself and others.
Whereas every rider of the vehicle already knows the rules
on the road but ignore it. As transportation system has
become increasingly intelligent with the rapid increase of
traffic demand in these years, applying Intelligent
Transportation System (ITS) technology becomes one of the
fundamental measures to make use of the existing
transportation infrastructures reasonably and scientifically.
Meanwhile, vehicle detection and classification technology is
an important component of Intelligent Transportation System,
which provides initial and necessary information of the traffic
for Intelligent Transportation System.
Up to date, there have been numbers of the proposed
approaches to discuss the problem of vehicle classification
[1]–[5]. Most of the proposed approaches can be seen as
sensor-based and visual-based approach. The sensor-based
approach needs some particular sensor installation in the road
networks. This method seems easy to implement but we need
consider some factor likes high cost, less flexibility in the
system, and the weather forecast. Generally, by using some
sensors installed on the road networks (e.g. magnetic sensor