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Received：12 September 2023，
Revised：2023-12-20，
Published：16 September 2024
移动端阅览
在智能交通系统中，车辆作为最普及的交通工具，常被不法分子利用，使其成为一种安全隐患，因此，实现监控设备下的车辆身份识别一直是一个研究热点。车辆标志（简称车标）是车辆的特殊身份，包含着车辆品牌制造商的基本信息，相比车牌、车型和车色，车标具有相对独立和可靠的特性。车辆标志识别能够快速、精准地缩小车辆查询范围，为案件侦破、交通自动化管理等有效降低搜索成本，因此车辆标志识别在车辆身份识别中尤其重要。本文对近十年内的主流车标识别方法进行了系统概述，为车标识别领域的后续研究者提供参考。1）简要阐述了在智能交通系统中车标识别技术的研究背景和重要性。2）根据车标识别过程中是否依赖手工提取特征，将目前国际主流的车标识别方法归纳为传统的车标识别方法和基于深度学习的车标识别方法，并分别总结了这两类方法的优劣。随后，分类、梳理和评价了这两类方法中现有的各种算法。3）针对车标数据集稀少导致难以评价各类算法性能、影响车标识别研究进展的问题，详细介绍了3种公开车标数据集：XMU（Xiamen University Vehicle Logo Dataset）、HFUT-VL（Vehicle Logo Dataset from Hefei University of Technology）和VLD-45（Vehicle Logo Dataset-45），并给出下载地址，可供研究者进行实验和测试。4）描述了4种常用的评价指标，并在公开数据集上基于这些评价指标对车标识别方法开展实验，并对实验结果进行比较和分析。5）综述现有车标识别技术中存在的一些问题与挑战，对未来车标识别的研究方向做出预测和展望。
In intelligent transportation systems （ITSs）， vehicles are the most popular means of transportation. However， they become a security risk due to the frequent use by lawless elements. Thus， vehicle identification with use of monitoring equipment has become a research hotspot. Vehicle logo is the special identity of the vehicle， and it contains basic information of a vehicle brand manufacturer. Compared with the license plate， model， and color of the vehicle， the vehicle logo is relatively independent and reliable. The recognition of vehicle logos rapidly and accurately narrows down the scope of vehicle search， which makes it important in vehicle identification. This paper presents a systematic overview of the mainstream methods of vehicle logo recognition from the last decade to provide a reference for researchers in the field. The initial discussion focuses on vehicle logo recognition， which is continuously under construction and development. Vehicle identification provides a strong support to the development and maturity of ITSs. Vehicle identity comprises four parts： vehicle logos， license plates， vehicle models， and vehicle colors. For the reduced algorithmic costs and increased accuracy of vehicle identity recognition， vehicle logo recognition is the most suitable to be implemented for current needs. Second， the current international mainstream methods for vehicle logo recognition fall under classical and deep learning-based approaches， depending on their reliance on manual feature extraction. This section summarizes the advantages， disadvantages， and main ideas of both types of methods. Classical methods for the recognition of vehicle logos can design proprietary solutions for problems specific to vehicle logo recognition. Such methods show minimal dependence on the number of training samples and had low hardware requirements. However， they require manual feature extraction and cannot learn vehicle logo features independently for automatic recognition. The classical method for vehicle logo recognition involves the following steps： inputting of the image， preprocessing operations， feature extraction， recognition of vehicle logos， and outputting of the final result with accuracy. Vehicle logo recognition based on deep learning methods circumvents the laborious manual feature extraction process and exhibits an improved performance when sufficient samples are available. However， this step incurs high computational costs and demands the use of advanced hardware. The main approach of this method entails the creation of a vehicle logo recognition module and a model training module via deep learning techniques. The logo recognition module requires inputting the logo image， followed by preprocessing operations. Logo recognition is then accomplished through the application of deep learning methods， and the final performance refers to the accurate output of recognition findings. The model training module requires the preparation of a substantial dataset， application of preprocessing operations， connection of the neural network structure for independent learning and feature extraction from vehicle logo images， and utilization of a classification network for the recognition and classification of vehicle logos. These methods are further subdivided into contemporary international mainstream techniques. Classical vehicle logo recognition methods fall under four types： those based on scale-invariant feature transform feature extraction， histogram-of-oriented-gradient feature extraction， invariant moments， and other classical recognition methods. In addition， vehicle logo recognition based on deep learning methods come in three types： those based on you-only-look-once series of algorithms， deep residual network algorithms， and other algorithms based on convolutional neural networks （CNNs）. This paper systematically sorted out the characteristics， advantages， and disadvantages of various algorithms and the datasets used in these methods. To reiterate， addressing the problem brought about by the scarcity of datasets on vehicle logos causes difficulty in the evaluation of the effectiveness of various algorithms and hinders the research on vehicle logos recognition. We explained in detail three publicly available vehicle logo datasets. Xiamen University Vehicle Logo Dataset （XMU）， Vehicle Logo Dataset from Hefei University of Technology （HFUT-VL）， and Vehicle Logo Dataset-45 （VLD-45） are available for researchers to conduct experiments and tests via the provided download addresses. In addition， we described four commonly used evaluation metrics and perform experiments on vehicle logo recognition methods based on these evaluation metrics using a publicly available dataset. Then， the results were compared and analyzed. Finally， regardless of excellent performance of conventional methods of vehicle logo recognition in small-sample environments and the numerous solutions proposed for certain complex environments， limitations were still encountered in complex and variable traffic situations. Although the use of a deep learning-based vehicle logo recognition method improved the recognition and robustness of the model after training， such an improvement came at the cost of training on a large-scale vehicle logo dataset and constantly updating hardware. By synthesizing the challenges faced by classical vehicle logo recognition methods in ITSs and vehicle logo recognition based on deep learning methods， this paper presents the following predictions and future development directions： 1） new algorithms can be developed for low-cost， highly robust， and efficient vehicle logo recognition for practical applications. Vehicle logo recognition represents a common image classification problem in complex traffic environments. This task inevitably faces severe challenges from various factors， such as lighting effects， inclination changes， occlusion， wear and tear， and extreme weather. The development of new algorithms that balance recognition accuracy and speed while reducing costs and complexity， which will expand the deployment scenarios of a model， remains a research direction worthy of continuous exploration. 2） Dynamic video research broadens the scope of applications in vehicle logo recognition. Vehicle logo recognition currently relies on static images， which presents challenges in data acquisition and expansion， consumes time and resources， and limits scalability and efficiency. Added complexity is encountered when dealing with multivehicle scenarios and continuous dynamic scenes. Dynamic video-based methods take advantage of easily collected video data and the capture of vehicle logos from diverse angles and environments. Consequently， video-based vehicle logo recognition opens avenues for future research with new opportunities and challenges. 3） Integration of the Transformer visual model improves the network structure to boost performance. Transformer neural networks， which show promise in recognition tasks， have gained attention for their exceptional representational capability and efficient processing of global information，. In contrast to CNNs， transformer visual models show excellent performance in image comprehension， global attention， and mitigation of feature loss. Thus， the incorporation of Transformer visual models in vehicle logo recognition research is of substantial value. 4） The combination of large artificial intelligence （AI） models improves cross-modal open-domain vehicle logo recognition via the integration of multimodal data for increased model robustness and accuracy. This approach assimilates vehicle logo features with associated textual data， such as manufacturer and model number， into a unified model to address limited multimodal information challenges. Large AI models effectively tackle data scarcity in the recognition of cross-modal open-domain decals and extract richer patterns from limited data to enhance the identification of unknown categories. Despite their powerful capabilities， deploying these models for vehicle logo recognition in open-domain scenarios poses financial challenges， which render their application a complex and cutting-edge task.
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