交通道路行驶车辆车标识别算法  

Logo recognition algorithm for vehicles on traffic road

作　　者：李讷 徐光柱[1,2] 雷帮军[1,2] 马国亮 石勇涛[1,2] LI Ne;XU Guangzhu;LEI Bangjun;MA Guoliang;SHI Yongtao(College of Computer and Information Technology,China Three Gorges University,Yichang Hubei 443002,China;Hubei Key Laboratory of Intelligent Vision Monitoring for Hydropower Engineering(China Three Gorges University),Yichang Hubei 443002,China;Traffic Police Detachment of Public Security Bureau of Yichang City,Yichang Hubei 443002,China)

机构地区：[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

出　　处：《计算机应用》2022年第3期810-817,共8页journal of Computer Applications

基　　金：湖北省中央引导地方科技发展专项(2019ZYYD007)。

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。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.

关 键 词：车标定位 车标识别 深度学习 特征提取 模板匹配 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]