Logo recognition algorithm for vehicles on traffic road

doi:10.11772/j.issn.1001-9081.2021040860

Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (3): 810-817.DOI: 10.11772/j.issn.1001-9081.2021040860

Special Issue: 人工智能； 2021年中国计算机学会人工智能会议(CCFAI 2021)

• 2021 CCF Conference on Artificial Intelligence (CCFAI 2021) • Previous Articles Next Articles

Logo recognition algorithm for vehicles on traffic road

Ne LI¹, Guangzhu XU¹^,²(), Bangjun LEI¹^,², Guoliang MA³, Yongtao SHI¹^,²

^1.College of Computer and Information Technology，China Three Gorges University，Yichang Hubei 443002，China
^2.Hubei Key Laboratory of Intelligent Vision Monitoring for Hydropower Engineering （China Three Gorges University），Yichang Hubei 443002，China
^3.Traffic Police Detachment of Public Security Bureau of Yichang City，Yichang Hubei 443002，China

Received:2021-05-25 Revised:2021-06-23 Accepted:2021-06-25 Online:2021-11-09 Published:2022-03-10
Contact: Guangzhu XU
About author:LI Ne， born in 1996， M. S. candidate. Her research interests include digital image processing， target detection.
LEI Bangjun， born in 1973， Ph. D.， professor. His research interests include artificial intelligence， computer vision.
MA Guoliang， born in 1974， engineer. His research interests include intelligent transportation.
SHI Yongtao，born in 1978， Ph. D.， associate professor. His research interests include digital image processing， pattern recognition.
Supported by:
Hubei Provincial Central Government Guiding Local Science and Technology Development Special Project(2019ZYYD007)

交通道路行驶车辆车标识别算法

李讷¹, 徐光柱¹^,²(), 雷帮军¹^,², 马国亮³, 石勇涛¹^,²

^1.三峡大学计算机与信息学院，湖北宜昌 443002
^2.水电工程智能视觉监测湖北省重点实验室（三峡大学），湖北宜昌 443002
^3.宜昌市公安交通警察支队，湖北宜昌 443002

通讯作者: 徐光柱
作者简介:李讷（1996—），女，山西长治人，硕士研究生，CCF会员，主要研究方向：数字图像处理、目标检测
雷帮军（1973—），男，湖北宜昌人，教授，博士，主要研究方向：人工智能、计算机视觉
马国亮（1974—），男，湖北宜昌人，工程师，主要研究方向：智能交通
石勇涛（1978—），男，湖北宜昌人，副教授，博士，主要研究方向：数字图像处理、模式识别。
基金资助:
湖北省中央引导地方科技发展专项(2019ZYYD007)

Abstract

Abstract:

In order to solve the problems of small targets， large noises， and many types in the logo recognition for vehicles on traffic road， a method combining a target detection algorithm based on deep learning and a template matching algorithm based on morphology was proposed， and a recognition system with high accuracy and capable of dealing with new types of vehicle logo was designed. First， K-Means++ was used to re-cluster the anchor box values and residual network was introduced into YOLOv4 for one-step positioning of the vehicle logo. Secondly， the binary vehicle logo template library was built by preprocessing and segmenting standard vehicle logo images. Then， the positioned vehicle logo was preprocessed by MSRCR （Multi-Scale Retinex with Color Restoration）， OTSU binarization， etc. Finally， the Hamming distance was calculated between the processed vehicle logo and the standard vehicle logo in the template library and the best match was found. In the vehicle logo detection experiment， the improved YOLOv4 detection achieves the higher accuracy of 99.04% compared to the original YOLOv4， two-stage positioning method of vehicle logo based on license plate position and the vehicle logo positioning method based on radiator grid background； its speed is slightly lower than that of the original YOLOv4， higher than those of the other two， reaching 50.62 fps （frames per second）. In the vehicle logo recognition experiment， the recognition accuracy based on morphological template matching is higher compared to traditional Histogram Of Oriented Gradients （HOG）， Local Binary Pattern （LBP） and convolutional neural network， reaching 91.04%. Experimental results show that the vehicle logo detection algorithm based on deep learning has higher accuracy and faster speed. The morphological template matching method can maintain a high recognition accuracy under the conditions of light change and noise pollution.

Key words: vehicle logo positioning, vehicle logo recognition, deep learning, feature extraction, template matching

摘要：

为解决交通道路行驶车辆车标识别中存在的目标小、噪声大、种类多的问题，提出了一种基于深度学习的目标检测算法与基于形态学模板匹配算法相结合的方法，并设计了一种高准确度且能应对新类型车标的识别系统。首先，采用通过K-Means++重新聚类锚框值，并引入残差网络的YOLOv4进行车标的一步定位；其次，通过对标准车标图像进行预处理及分割，构建二值车标模板库；接着，利用带色彩恢复的多尺度视网膜图像增强算法（MSRCR）、最大类间方差法（OTSU）等对定位到的车标进行预处理；最后，将处理好的车标与模板库中的标准车标进行汉明距离计算，求出最佳匹配。车标检测实验中，改进的YOLOv4检测精度均优于原始YOLOv4、基于车牌位置的车标两步定位法和基于散热器栅格背景的车标定位法，达到99.04%；速度略低于原始YOLOv4，高于另外两者，达到每秒50.62帧。车标识别实验中基于形态学模板匹配的识别精度均高于传统的方向梯度直方图（HOG）、局部二值模式（LBP）和卷积神经网络，达到92.68%。实验结果表明基于深度学习的车标检测算法有较高的精度和较快的速度，形态学模板匹配方法在光照变化和噪声污染的情况下仍能保持较高的识别精度。

关键词: 车标定位, 车标识别, 深度学习, 特征提取, 模板匹配

CLC Number:

TP391

Ne LI, Guangzhu XU, Bangjun LEI, Guoliang MA, Yongtao SHI. Logo recognition algorithm for vehicles on traffic road[J]. Journal of Computer Applications, 2022, 42(3): 810-817.

李讷, 徐光柱, 雷帮军, 马国亮, 石勇涛. 交通道路行驶车辆车标识别算法[J]. 《计算机应用》唯一官方网站, 2022, 42(3): 810-817.

Figures/Tables 24

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数据集类型	训练集	测试集
车辆正面	18 000	6 500
车辆背面	8 000	2 500
车辆侧面	4 000	1 000

数据集类型	训练集	测试集
车辆正面	18 000	6 500
车辆背面	8 000	2 500
车辆侧面	4 000	1 000

算法	准确率/%	召回率/%	速度/fps
YOLOv4	97.38	97.43	50.64
K-Means++_YOLOv4	98.04	98.00	50.64
ResNet_YOLOv4	98.37	98.40	50.62
本文算法	99.04	98.27	50.62

算法	准确率/%	召回率/%	速度/fps
YOLOv4	97.38	97.43	50.64
K-Means++_YOLOv4	98.04	98.00	50.64
ResNet_YOLOv4	98.37	98.40	50.62
本文算法	99.04	98.27	50.62

算法	测试集	正确数	准确率/%	平均时间/s
文献［2］算法	200	168	84.0	0.862 0
文献［4］算法	200	173	86.5	1.224 6
YOLOv4	200	197	98.5	0.005 0
本文算法	200	200	100.0	0.005 2

Logo recognition algorithm for vehicles on traffic road

交通道路行驶车辆车标识别算法

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Figures/Tables 24

References 32

Related Articles 15

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Metrics

序号	车标名称	测试集样本数	模板匹配		DenseNet201
序号	车标名称	测试集样本数	正确数	准确率/%	正确数	准确率/%
1	讴歌	116	116	100.00	101	87.07
2	北京汽车	102	87	85.29	85	83.33
3	北汽幻速	174	157	90.23	158	90.80
4	宝骏	159	147	92.45	140	88.05
5	宝马	173	163	94.22	153	88.44
6	比亚迪	132	115	87.12	104	78.79
7	凯迪拉克	100	86	86.00	86	86.00
8	长安之星	130	118	90.77	113	86.92
9	奇瑞	121	103	85.12	96	79.34
10	沃尔沃	172	168	97.67	160	93.02
11	广汽传祺	124	116	93.55	113	91.13
12	铃木	166	161	96.99	154	92.77
13	江淮	231	208	90.04	227	98.27
14	东风	131	115	87.79	121	92.37
15	福田	122	112	91.80	109	89.34
16	三菱	180	178	98.89	169	93.89
17	尼桑	194	184	94.85	181	93.30

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序号	车标名称	测试集样本数	准确率/%
序号	车标名称	测试集样本数	HOG	LBP	DenseNet201	本文方法
平均准确率			86.59	90.87	92.16	92.68
1	奥迪	150	86.67	88.67	88.00	92.00
2	中国一汽	150	87.33	89.33	88.67	88.00
3	雪铁龙	200	88.00	90.00	95.00	86.00
4	大众	200	95.00	94.00	98.50	99.00
5	现代	150	75.00	93.33	94.00	93.33
6	雪佛兰	150	80.00	90.67	95.33	96.00
7	长安	200	85.00	92.50	90.50	93.50
8	丰田	100	87.00	89.00	94.00	87.00
9	本田	200	91.00	89.50	86.50	93.50
10	奔驰	150	90.67	92.00	85.33	89.33
11	长城	150	92.00	87.33	96.00	93.33
12	马自达	200	74.00	89.00	97.00	94.50
13	五菱宏光	150	94.00	96.00	89.33	99.33

序号	车标名称	测试集样本数	准确率/%
序号	车标名称	测试集样本数	HOG	LBP	DenseNet201	本文方法
平均准确率			86.59	90.87	92.16	92.68
1	奥迪	150	86.67	88.67	88.00	92.00
2	中国一汽	150	87.33	89.33	88.67	88.00
3	雪铁龙	200	88.00	90.00	95.00	86.00
4	大众	200	95.00	94.00	98.50	99.00
5	现代	150	75.00	93.33	94.00	93.33
6	雪佛兰	150	80.00	90.67	95.33	96.00
7	长安	200	85.00	92.50	90.50	93.50
8	丰田	100	87.00	89.00	94.00	87.00
9	本田	200	91.00	89.50	86.50	93.50
10	奔驰	150	90.67	92.00	85.33	89.33
11	长城	150	92.00	87.33	96.00	93.33
12	马自达	200	74.00	89.00	97.00	94.50
13	五菱宏光	150	94.00	96.00	89.33	99.33