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Low - resolution vehicle recognition based on deep feature fusion

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Abstract

Recently, convolutional neural networks have achieved great success in image classification. However, the traditional convolutional neural network lacks the ability to distinguish image features, especially for the low resolution images with less feature information. In the vehicle recognition task, it is inevitable to lose some feature information by convolution during the process of the low-level feature is abstracted into the high-level semantic feature. In this paper, an improved convolutional neural network model with higher robustness is proposed, we call it feature fusion convolutional neural network (FFCNN), which can not only produce more discriminative features, but also can avoid interference caused by environmental factors to some extent. Firstly, the strategy of feature fusion is used to fuse the different low-level features in the convolution neural network. Secondly, in order to prevent overfitting, we combine with the network model of sparse and data augmentation to optimize the structure of the network model. The results of the experiment show that the model proposed in this paper has higher recognition accuracy compared with the traditional vehicle recognition methods and the original convolutional neural network models.

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Acknowledgments

We express our sincere thanks to the anonymous reviewers for their useful comments and suggestions to raise the standard of the paper. This study was supported by the National Natural Science Foundation of China under Grant No. 61672202.

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Authors and Affiliations

  1. School of Computer and Information, Hefei University of Technology, Hefei, China

    Lixia Xue, Xin Zhong, Ronggui Wang, Juan Yang & Min Hu

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  1. Lixia Xue
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  2. Xin Zhong
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  3. Ronggui Wang
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  4. Juan Yang
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  5. Min Hu
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Correspondence to Juan Yang.

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Xue, L., Zhong, X., Wang, R. et al. Low - resolution vehicle recognition based on deep feature fusion. Multimed Tools Appl 77, 27617–27639 (2018). https://doi.org/10.1007/s11042-018-5940-6

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  • DOI: https://doi.org/10.1007/s11042-018-5940-6

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