Fine-grained Classification of Car Models Using Fg-CarNet Convolutional Neural Network
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摘要: 车辆型号识别在智能交通系统、涉车刑侦案件侦破等方面具有十分重要的应用前景.针对车辆型号种类繁多、部分型号区分度小等带来的车辆型号精细分类困难的问题，采用车辆正脸图像为数据源，提出一种多分支多维度特征融合的卷积神经网络模型Fg-CarNet（Convolutional neural networks for car fine-grained classification，Fg-CarNet）.该模型根据车正脸图像特征分布特点，将其分为上下两部分并行进行特征提取，并对网络中间层产生的特征进行两个维度的融合，以提取有区分度的特征，提高特征表达能力，通过使用小卷积核以及全局均值池化，使在网络分类准确度提高的同时降低了网络模型参数大小.在CompCars数据集上进行验证，实验结果表明，Fg-CarNet提取的车辆特征在保证网络模型参数最小的同时，车辆型号识别率达到最高，实现了最好的分类效果.Abstract: Car model recognition has very important application in intelligent transportation systems and vehicle-related criminal case detection. A multi-branch and multi-dimension feature fusion convolutional neural network (CNN) model, Fg-CarNet (convolutional neural networks for car fine-grained classification), is proposed. This model uses car frontal face images as data source, and aims to solve the classification difficulty caused by the wide variety of car models and little differentiation between some models. Based on the image feature distribution characteristic of frontal face images, the Fg-CarNet divides them into upper parts and lower parts to extract features in parallel, and then merges the features generated by middle layers of the network to extract more distinguishing features. Through using small convolution kernel and global average pooling, the classification accuracy of Fg-CarNet is improved and at the same time the size of network parameters is reduced. With CompCars dataset, experiments are carried out. The results show that the proposed method can achieve the highest recognition accuracy while keeping the smallest size of network parameters, i.e, the method can achieve the best classification result.1) 本文责任编委 赖剑煌
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图 3 三类神经网络模型中层激活值可视化图
Fig. 3 Visualization of the layer activations in three neural network models
表 1 Fg-CarNet模型结构参数
Table 1 tructural parameters of the Fg-CarNet
子网络 层编号 类型 卷积核尺寸/步长 池化类型 池化尺寸和步长 输出尺寸(深度$\times$长度$\times$高度) UpNet 1 Convolution/BN 5$\times$5/2 Max pooling 3$\times$3/2 64$\times$64$\times$32 2 Convolution/BN 3$\times$3/1 Max pooling 3$\times$3/2 96$\times$32$\times$16 3 Convolution/BN 3$\times$3/1 Max pooling 3$\times$3/2 128$\times$16$\times$8 4 Convolution/BN 3$\times$3/1 Max pooling 3$\times$3/2 128$\times$8$\times$4 DownNet 1 Convolution 5$\times$5/2 $-$ $-$ 64$\times$128$\times$64 2 Convolution/BN 1$\times$1/1 Max pooling 3$\times$3/2 64$\times$64$\times$32 3 Convolution 3$\times$3/1 $-$ $-$ 96$\times$64$\times$32 4 Convolution/BN 1$\times$1/1 Max pooling 3$\times$3/2 96$\times$32$\times$16 5 Convolution 3$\times$3/1 $-$ $-$ 128$\times$32$\times$16 6 Convolution/BN 1$\times$1/1 Max pooling 3$\times$3/2 128$\times$16$\times$8 7 Convolution 3$\times$3/1 $-$ $-$ 128$\times$16$\times$8 8 Convolution/BN 1$\times$1/1 Max pooling 3$\times$3/2 128$\times$8$\times$4 FusionNet 1 Concat $-$ $-$ 96$\times$32$\times$32 2 Convolution 3$\times$3/2 Max pooling 2$\times$2/2 128$\times$8$\times$8 3 Concat $-$ $-$ $-$ 128$\times$8$\times$8 4 Concat $-$ $-$ $-$ 256$\times$8$\times$8 5 Convolution/BN 3$\times$3/1 Max pooling 3$\times$3/2 256$\times$4$\times$4 6 Convolution/Drop 1$\times$1/1 $-$ $-$ 281$\times$4$\times$4 7 Convolution/Drop 1$\times$1/1 $-$ $-$ 281$\times$4$\times$4 8 Convolution 1$\times$1/1 Global pooling $-$ 281$\times$1$\times$1 
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表 2 卷积神经网络模型在CompCars上使用不同分类器的识别率
Table 2 Recognition rate of different CNN models using different classifiers on CompCars
分类器 各模型识别率 AlexNet (%) GoogLeNet (%) NIN (%) Fg-CarNet (%) 朴素贝叶斯 91.10 96.95 86.06 98.42 KNN 93.41 98.35 92.78 98.78 逻辑回归 96.08 98.39 95.91 98.76 随机森林 82.96 95.61 74.60 93.52 SVM 96.02 98.33 96.23 98.78 Softmax 97.73 98.50 96.51 98.89
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表 3 各神经网络模型参数的大小
Table 3 The size of each CNN model parameters
神经网络模型 模型参数大小(MB) AlexNet 232.1 GoogLeNet 44.7 NIN 12.8 Fg-CarNet 6.3
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表 5 分块融合的性能比较
Table 5 Performance comparison of block fusion
模型 准确率1 (%) 准确率2 (%) Fg-CarNet-Up 93.37 89.78 Fg-CarNet-Down 97.38 93.82 Fg-CarNet-Whole 98.02 97.84 Fg-CarNet 98.89 98.27
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表 6 不同基本单元特征组合下的识别结果
Table 6 Recognition result based on different basic unit combinations
模型编号 单元编号 1 2 3 4 准确率1 1 √ √ 0.98906 2 √ √ 0.98789 3 √ √ 0.98843 4 √ √ √ 0.98835 5 √ √ √ 0.98901 6 √ √ √ 0.98882 7 √ √ √ √ 0.98835
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