A Novel Fine-Grained Method for Vehicle Type Recognition Based on the Locally Enhanced PCANet Neural Network
A Novel Fine-Grained Method for Vehicle Type Recognition Based on the Locally Enhanced PCANet Neural Network
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DOI：
10.1007/s11390-018-1822-7
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发表时间：
2018-03
影响因子：
0.7
通讯作者：
Qian Wang;Youdong Ding
中科院分区：
文献类型：
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作者：
Qian Wang;Youdong Ding
在本文中，我们提出了一个局部增强的PCANet神经网络的细粒度分类的车辆。与大多数最先进的机器学习方法相比，所提出的方法采用具有较少层数和简单参数的PCANet无监督网络。它简化了计算步骤和手动标记，并且无需耗时的培训即可识别车辆类型。实验结果表明，与传统的模式识别方法和多层CNN方法相比，该方法在样本库规模、角度偏差和训练速度等方面达到了最优平衡。它还表明，引入适当的局部特征，具有不同的尺度从一般特征是非常有助于提高识别率。实践证明，180 ° 7角（360° 12角）分类建模方案是一种有效的方法，可以解决因角度偏差而导致识别率下降的问题，提高实际识别的准确率。
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.