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浙江大学学报(工学版)
浙江大学学报(工学版)  2022, Vol. 56 Issue (4): 775-782    DOI: 10.3785/j.issn.1008-973X.2022.04.017
计算机技术、信息工程     
融合注意力机制的高效率网络车型识别
柳长源1(),何先平1,毕晓君2
1. 哈尔滨理工大学 测控技术与通信工程学院，黑龙江 哈尔滨 150080
2. 中央民族大学 信息工程学院，北京 100081
Efficient network vehicle recognition combined with attention mechanism
Chang-yuan LIU1(),Xian-ping HE1,Xiao-jun BI2
1. College of Measurement and Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China
2. School of Information Engineering, Minzu University of China, Beijing 100081, China
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摘要：

为了解决现有的车型识别算法对车型特征描述不充分的情况，提出融合注意力机制的高效率网络车型识别算法. 利用高效率网络中的复合缩放方式来平衡网络的深度、宽度和分辨率，将深度可分离卷积集成到基础特征提取模块中来提高模型准确率. 增加双通道的残差注意力机制来关注图片中的关键信息，获得含有更加丰富语义信息的特征图. 在网络的末端添加单独的softmax分类器，使用标签平滑正则化对损失函数进行处理，减小模型过拟合的问题. 在BIT-Vehicles数据集上进行实验，结果表明，提出方法的平均分类准确率为96.83%，较改进前的模型提高了1.11%，优于现有DCNN、Faster-CNN的改进算法，较Faster R-CNN提升了7.16%.
关键词： 车型识别;  高效率网络;  残差注意力机制;  标签平滑正则化;  深度可分离卷积    
Abstract:

An efficient network vehicle recognition algorithm combined with attention mechanism was proposed in order to solve the problem that the existing vehicle type recognition algorithm does not adequately describe the vehicle type characteristics. The depth, width and resolution of the network were balanced by the compound scaling method in the efficient network, and the depth separable convolution was integrated into the basic feature extraction module in order to improve the accuracy of the model. The residual attention mechanism of two channels was added to pay attention to the key information in the picture, and the feature map with richer semantic information was obtained. A separate softmax classifier was added at the end of the network, and the label smoothing regularization was used to deal with the loss function in order to reduce the problem of model over-fitting. Experiments on BIT-Vehicles data set showed that the average classification precision of the proposed method was 96.83%, which was 1.11% higher than that of the original model, and was better than the existing improved algorithms of DCNN and Faster-CNN and 7.16% higher than Faster R-CNN.
Key words: vehicle type identification    high efficiency network    residual attention mechanism    label smoothing regularization    depth separable convolution
收稿日期: 2021-05-10 出版日期: 2022-04-24
CLC:  TP 391  
基金资助: 国家自然科学基金资助项目(51779050)； 黑龙江省自然科学基金资助项目(F2016022)
作者简介: 柳长源（1970—），男，副教授，从事模式识别与机器学习的研究. orcid.org/0000-0003-2204-0612. E-mail： liuchangyuan@hrbust.edu.cn
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引用本文:

柳长源,何先平,毕晓君. 融合注意力机制的高效率网络车型识别[J]. 浙江大学学报(工学版), 2022, 56(4): 775-782.

Chang-yuan LIU,Xian-ping HE,Xiao-jun BI. Efficient network vehicle recognition combined with attention mechanism. Journal of ZheJiang University (Engineering Science), 2022, 56(4): 775-782.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2022.04.017    或    https://www.zjujournals.com/eng/CN/Y2022/V56/I4/775

EfficientNet w d r dr
B0 1.0 1.0 224 0.2
B1 1.0 1.1 240 0.2
B2 1.1 1.2 260 0.3
B3 1.2 1.4 300 0.3
B4 1.4 1.8 380 0.4
B5 1.6 2.2 456 0.4
B6 1.8 2.6 528 0.5
B7 2.0 3.1 600 0.5
表 1  EfficientNet缩放参数
图 1  MBConv卷积块结构
阶段 参数 通道数 分辨率
1 Conv3×3 32×1 224×224
2 MBConv1, 3×3 16×1 112×112
3 MBConv6, 3×3 24×2 112×112
4 MBConv6, 5×5 40×2 56×56
5 MBConv6, 3×3 80×3 28×28
6 MBConv6, 5×5 112×3 14×14
7 MBConv6, 5×5 192×4 14×14
8 MBConv6, 3×3 320×1 7×7
9 Conv1×1×1, Pooling 1280×1 7×7
10 FC, softmax 1280×1 7×7
表 2  EfficientNet-B0网络参数结构
图 2  通道注意力模块
图 3  空间注意力模块
图 4  双通道残差注意力模块
类别 数量 y
公共汽车(Bus) 558 0
微型客车(Microbus) 883 1
小货车(Minivan) 475 2
小轿车(Sedan) 5921 3
越野车(SUV) 1392 4
卡车(Truck) 822 5
表 3  BIT-Vehicles数据集分布
图 5  6类车型的图像示例
真实结果 预测结果
正例 反例
正例 TP FN
反例 FP TN
表 4  二分类混淆矩阵
EfficientNet Acc /% P/106
B0 93.17 15.59
B1 91.17 25.26
B2 92.83 29.81
B3 90.17 41.33
B4 93.00 67.65
B5 92.50 109.05
B6 91.00 156.56
B7 93.00 224.88
表 5  EfficientNet-B0~B7的训练结果
类别 Acc /%
Efficient
Net-B0 		B0+LSR B0+softmax B0+残差
CBAM 		本文方法
Bus 99.90 99.90 99.90 99.99 99.99
Microbus 95.60 96.20 97.00 96.60 98.20
Minivan 99.80 99.80 99.80 99.99 99.99
Sedan 98.60 98.60 98.00 97.80 98.40
SUV 84.80 82.80 84.60 85.60 86.60
Truck 95.40 97.00 96.00 96.00 97.80
表 6  不同模型下的车型分类准确率
图 6  改进前、后的模型损失收敛曲线
模型 T/ms
EfficientNet-B0 8.9825
B0+LSR 10.9409
B0+softmax 12.3933
B0+残差CBAM 12.1856
本文方法 11.9415
表 7  单张图片的识别时间
真实标签 预测值
y = 0 y = 1 y = 2 y = 3 y = 4 y = 5
0 100 0 0 0 0 0
1 0 96 0 2 2 0
2 0 0 100 0 0 0
3 0 0 0 98 2 0
4 0 6 0 9 85 0
5 0 0 5 0 0 95
表 8  EfficientNet-B0的混淆矩阵
真实标签 预测值
y = 0 y = 1 y = 2 y = 3 y = 4 y = 5
0 100 0 0 0 0 0
1 0 98 0 1 1 0
2 0 0 100 0 0 0
3 0 0 0 98 2 0
4 0 4 0 9 87 0
5 0 0 2 0 0 98
表 9  改进后模型的混淆矩阵
图 7  不同算法下的分类准确率
图 8  不同算法下的平均准确率
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[1] 袁公萍, 汤一平, 韩旺明, 陈麒. 基于深度卷积神经网络的车型识别方法[J]. 浙江大学学报(工学版), 2018, 52(4): 694-702.