Automatic vehicle classification using center strengthened convolutional neural network

Kuan Chung Wang, Yoga Dwi Pranata, Jia Ching Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1075-1078
Number of pages4
ISBN (Electronic)9781538615423
DOIs
StatePublished - 5 Feb 2018
Event9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017 - Kuala Lumpur, Malaysia
Duration: 12 Dec 201715 Dec 2017

Publication series

NameProceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
Volume2018-February

Conference

Conference9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
Country/TerritoryMalaysia
CityKuala Lumpur
Period12/12/1715/12/17

Keywords

  • Convolutional Neural Network
  • Deep learning
  • ROI pooling
  • Vehicle classification

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