基于FR-ResNet的车辆型号精细识别研究

余烨; 傅云翔; 杨昌东; 路强

doi:10.16383/j.aas.c180539

基于FR-ResNet的车辆型号精细识别研究

doi: 10.16383/j.aas.c180539

余烨^{1, 2, ,},
傅云翔^1,,
杨昌东^1,,
路强^{1, 2,}

1.
大数据知识工程教育部重点实验室(合肥工业大学) 合肥 230009
2.
合肥工业大学计算机与信息学院合肥 230009

基金项目:

国家自然科学基金 61906061

安徽省重点研究和开发计划项目 201904d07020010

详细信息

作者简介:
傅云翔   合肥工业大学计算机与信息学院硕士研究生. 2016年获得合肥工业大学计算机与信息学院学士学位. 主要研究方向为图像处理, 计算机视觉与深度学习.E-mail: yasinfu@mail.hfut.edu.cn

杨昌东   合肥工业大学计算机与信息学院硕士研究生. 2017年获得黄山学院信息工程学院学士学位. 主要研究方向为图像处理, 计算机视觉与深度学习.E-mail: 2017170766@mail.hfut.edu.cn

路强   合肥工业大学计算机与信息学院副教授. 2010年获合肥工业大学计算机科学与技术专业博士学位. 主要研究方向为可视化, 协同计算与图像处理.E-mail: luqiang@hfut.edu.cn

通讯作者:
余烨合肥工业大学计算机与信息学院副教授. 2010年获合肥工业大学计算机科学与技术专业博士学位. 主要研究方向为图像处理, 计算机视觉, 虚拟现实与可视化. 本文通信作者.E-mail: yuye@hfut.edu.cn

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出版历程
- 收稿日期: 2018-08-08
- 录用日期: 2019-01-18
- 刊出日期: 2021-05-21

Fine-Grained Car Model Recognition Based on FR-ResNet

YU Ye^{1, 2
, ,},
FU Yun-Xiang^1
,,
YANG Chang-Dong^1
,,
LU Qiang^{1, 2
,}

1.
Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology), Hefei 230009
2.
School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009

Funds:

National Natural Science Foundation of China 61906061

Provincial Key Research and Development Program of Anhui Province 201904d07020010

More Information

Author Bio:
FU Yun-Xiang   Master student at the School of Computer Science and Information, Hefei University of Technology. He received his bachelor degree from Hefei University of Technology in 2016. His research interest covers image processing, computer vision, and deep learning

YANG Chang-Dong   Master student at the School of Computer Science and Information, Hefei University of Technology. He received his bachelor degree from Huangshan University in 2017. His research interest covers image processing, computer vision, and deep learning

LU Qiang   Associate professor at the School of Computer Science and Information, Hefei University of Technology. He received his Ph. D. degree from Hefei University of Technology in 2010. His research interest covers visualization, cooperative computing, and image processing

Corresponding author: YU Ye Associate professor at the School of Computer Science and Information, Hefei University of Technology. She received her Ph. D. degree from Hefei University of Technology in 2010. Her research interest covers image processing, computer vision, and virtual reality and visualization. Corresponding author of this paper

摘要

摘要: 车辆型号精细识别的关键是提取有区分性的细节特征. 以"特征重用"为核心, 以有效提取车辆图像细节特征并进行高效利用为目的, 提出了一种基于残差网络特征重用的深度卷积神经网络模型FR-ResNet (Improved ResNet focusing on feature reuse). 该网络以ResNet残差结构为基础, 分别采用多尺度输入、低层特征在高层中重用和特征图权重学习策略来实现特征重用. 多尺度输入可以防止网络过深导致性能退化以及陷入局部最优; 对各层网络部分加以不同程度的特征重用, 可以加强特征传递, 高效利用特征并降低参数规模; 在中低层网络部分采用特征图权重学习策略, 可以有效抑制冗余特征的比重. 在公开车辆数据集CompCars和StanfordCars上进行实验, 并与其他的网络模型进行比较, 实验结果表明FR-ResNet在车辆型号精细识别任务中对车辆姿态变化和复杂背景干扰等具有鲁棒性, 获得了较高的识别准确率.
- 车辆型号精细识别 /
- 卷积神经网络 /
- 残差结构 /
- 特征重用
Abstract: The key of fine-grained car model recognition is to extract the discriminative feature details. Focusing on "feature reuse", aiming at efficiently extracting and using the feature details from car images, a deep convolutional neural network model named FR-ResNet (improved ResNet focusing on feature reuse) is proposed. Based on the residual structure, FR-ResNet adopts the strategy of multi-scale input, reuse of low level feature in high level network, and weight learning of feature maps to realize feature reuse. Multi-scale input can prevent performance degradation caused by too deep network and fall into local optimum. Different degrees of feature reuse for high, medium and low level network can enhance the feature transfer, efficiently reuse the features and reduce the size of parameters. The strategy of feature map weight learning applied on middle and low level network can effectively suppress the proportion of redundant features. Experimental results carried out on the state-of-the-art public vehicle datasets CompCars and StanfordCars show that, compared with other network models, FR-ResNet can obtain high recognition accuracy in car model recognition task, and is robust in the shooting angle of vehicles and the change of background.
- Fine-grained classification of car models /
- convolutional neural network (CNN) /
- residual structure /
- feature reuse
Recommended by Associate Editor BAI Xiang
注释:

1) 本文责任编委白翔

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图 1 相似车型的例子

Fig. 1 Examples of similar vehicle models

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图 2 残差函数拟合关系

Fig. 2 Fitting relationship of residual function

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图 3 FR-ResNet中的残差结构

Fig. 3 Residual structure in FR-ResNet

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图 4 网络结构示意图

Fig. 4 Diagram of network structure

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图 5 特征图可视化

Fig. 5 Visualization of feature maps

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图 6 特征重用过程

Fig. 6 The process of feature reuse

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图 7 特征图权重学习

Fig. 7 Weight learning based on feature maps

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图 8 数据集中的样本

Fig. 8 Samples from datasets

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图 9 识别错误的样本

Fig. 9 Samples of error recognition

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图 10 各结构性能比较

Fig. 10 Comparison of performances of all structures

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表 1 在StanfordCars数据集上的实验结果比较(%)

Table 1 Comparison of classification results on the StanfordCars dataset (%)

模型方法	准确率(有BBox)	准确率(无BBox)
BB-3D-G^[21]	67.6	-
LLC^[31]	69.5	-
ELLF^[32]	73.9	-
VGGNet^[13]	-	75.6
ResNet	85.8	74.8
FCANS^[33]	91.3	89.1
Wang等^[34]	92.5	-
Krause等^[35]	92.8	-
FR-ResNet	93.1	90.6

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表 2 在CompCars数据集上的实验结果比较(%)

Table 2 Comparison of classification results on the CompCars dataset (%)

模型方法	Top-1准确率	Top-5准确率
Yang等^[36]	76.7	91.7
AlexNet	81.9	90.4
BoxCars^[22]	84.8	95.4
GoogLeNet^[37]	91.2	98.1
ResNet	92.3	98.4
DenseNet^[38]	93.1	98.6
Hu等^[27]	94.3	98.9
FR-ResNet	95.3	99.1

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表 3 在CompCars少量样本数据集上的实验结果比较(%)

Table 3 Comparison of classification results on the small training samples from CompCars dataset (%)

模型方法	少量样本集$A$		少量样本集$B$
模型方法	Top-1准确率	Top-5准确率	Top-1准确率	Top-5准确率
GoogLeNet	65.8	87.9	52.3	76.7
ResNet	78.3	93.5	69.7	82.3
DenseNet	90.6	98.0	81.5	90.1
FR-ResNet	92.5	98.4	85.2	93.8

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表 4 特征重用比例$P$值对准确率的影响

Table 4 Effect of feature reuse ratio $P$ on recognition accuracy

	第1阶段			第2阶段			第3阶段					第4阶段
$P$值	1/64	1/16	1/8	1/64	1/16	1/8	1/64	1/32	3/64	1/16	1/8	1/64	1/16	1/8
准确率(%)	93.6	93.7%	93.4	93.6	94.1%	93.9	94.3	94.6%	94.2	94.2	94.0	94.6	94.8%	94.5%

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表 5 权重学习中池化策略的对比(%)

Table 5 Comparison results of pooling strategies in weight learning (%)

池化选择策略	Top-1准确率
全局平均池化	93.3
全局最大值池化	92.3
局部平均池化	92.5
局部最大值池化	92.6
全局平均+ 局部平均	93.4
全局平均+ 局部最大值	93.6
全局最大值+ 局部平均	93.1
全局最大值+ 局部最大值	92.6

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