Convolution neural network for unbalanced vehicle type recognition

Ye Ji, Ya Ping Dai, Meng Wang*, Yu Hui Li

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

Abstract

The convolution neural network in deep learning has been widely used in image recognition in recent years.For the traditionalvehicle type recognitionmethod by the vehicle scene and the bottom of the feature extraction and other limitations, has been unable toapractical application of the recognitionof the problem.Using the GoogLeNet network of the deep learning Caffe framework and the supervised Kohonen network with the traditional method, the vehicle image data sets (unbalanced vehicle type original image) and vehicle size feature data sets for network training and testing.The accuracy of the two models is compared and verified, and the applicability of the two methods is compared and analyzed.The experimental results show that the classification method of convolution neural network hasahigh accuracy of vehicle type recognitionunder the condition of large data volume.

Original languageEnglish
Publication statusPublished - 2017
Event5th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2017 - Beijing, China
Duration: 2 Nov 20175 Nov 2017

Conference

Conference5th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2017
Country/TerritoryChina
CityBeijing
Period2/11/175/11/17

Keywords

  • Convolutional neural network
  • Deep learning
  • GoogLeNet
  • Supervised Kohonen
  • Vehicletype recognition

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Ji, Y., Dai, Y. P., Wang, M., & Li, Y. H. (2017). Convolution neural network for unbalanced vehicle type recognition. Paper presented at 5th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2017, Beijing, China.