Department of Image, Chung-Ang University, Seoul 06974, Korea.
Sensors (Basel). 2020 May 9;20(9):2715. doi: 10.3390/s20092715.
To encourage people to save energy, subcompact cars have several benefits of discount on parking or toll road charge. However, manual classification of the subcompact car is highly labor intensive. To solve this problem, automatic vehicle classification systems are good candidates. Since a general pattern-based classification technique can not successfully recognize the ambiguous features of a vehicle, we present a new multi-resolution convolutional neural network (CNN) and stochastic orthogonal learning method to train the network. We first extract the region of a bonnet in the vehicle image. Next, both extracted and input image are engaged to low and high resolution layers in the CNN model. The proposed network is then optimized based on stochastic orthogonality. We also built a novel subcompact vehicle dataset that will be open for a public use. Experimental results show that the proposed model outperforms state-of-the-art approaches in term of accuracy, which means that the proposed method can efficiently classify the ambiguous features between subcompact and non-subcompact vehicles.
为鼓励人们节约能源，微型车在停车或收费公路方面有一些折扣优惠。然而，微型车的手动分类非常耗费人力。为了解决这个问题，自动车辆分类系统是很好的选择。由于基于一般模式的分类技术不能成功地识别车辆的模糊特征，因此我们提出了一种新的多分辨率卷积神经网络（CNN）和随机正交学习方法来训练网络。我们首先从车辆图像中提取引擎盖区域。然后，提取和输入图像都参与到 CNN 模型的低分辨率和高分辨率层中。然后，根据随机正交性对网络进行优化。我们还构建了一个新的微型车数据集，供公众使用。实验结果表明，与最先进的方法相比，所提出的模型在准确性方面表现更好，这意味着所提出的方法可以有效地对微型车和非微型车之间的模糊特征进行分类。
