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A Novel Fine-Grained Method for Vehicle Type Recognition Based on the Locally Enhanced PCANet Neural Network

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Abstract

In this paper, we propose a locally enhanced PCANet neural network for fine-grained classification of vehicles. The proposed method adopts the PCANet unsupervised network with a smaller number of layers and simple parameters compared with the majority of state-of-the-art machine learning methods. It simplifies calculation steps and manual labeling, and enables vehicle types to be recognized without time-consuming training. Experimental results show that compared with the traditional pattern recognition methods and the multi-layer CNN methods, the proposed method achieves optimal balance in terms of varying scales of sample libraries, angle deviations, and training speed. It also indicates that introducing appropriate local features that have different scales from the general feature is very instrumental in improving recognition rate. The 7-angle in 180° (12-angle in 360°) classification modeling scheme is proven to be an effective approach, which can solve the problem of suffering decrease in recognition rate due to angle deviations, and add the recognition accuracy in practice.

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Authors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, Shanghai, 200072, China

    Qian Wang

  2. Information Center, Criminal Investigation Department of Shanghai Public Security Bureau, Shanghai, 200083, China

    Qian Wang

  3. Shanghai Engineering Research Center of Motion Picture Special Effects, Shanghai, 200072, China

    Qian Wang & You-Dong Ding

  4. Shanghai Film Academy, Shanghai University, Shanghai, 200072, China

    You-Dong Ding

Authors
  1. Qian Wang
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  2. You-Dong Ding
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Correspondence to Qian Wang.

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Wang, ., Ding, YD. A Novel Fine-Grained Method for Vehicle Type Recognition Based on the Locally Enhanced PCANet Neural Network. J. Comput. Sci. Technol. 33, 335–350 (2018). https://doi.org/10.1007/s11390-018-1822-7

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  • DOI: https://doi.org/10.1007/s11390-018-1822-7

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