浏览全部资源
扫码关注微信
收稿：2023-09-12，
修回：2023-12-20，
纸质出版：2024-09-16
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.
Agarwal A ， Shinde S ， Mohite S and Jadhav S . 2022 . Vehicle characteristic recognition by appearance： computer vision methods for vehicle make， color， and license plate classification // Proceedings of 2022 IEEE Pune Section International Conference （PuneCon） . Pune， India ： IEEE： 1 - 6 ［ DOI： 10.1109/PuneCon55413.2022.10014731 http://dx.doi.org/10.1109/PuneCon55413.2022.10014731 ］
Agrawal S and Chaurasiya R K . 2017 . Automatic traffic sign detection and recognition using moment invariants and support vector machine // Proceedings of 2017 International Conference on Recent Innovations in Signal processing and Embedded Systems （RISE） . Bhopal， India ： IEEE： 289 - 295 ［ DOI： 10.1109/RISE.2017.8378169 http://dx.doi.org/10.1109/RISE.2017.8378169 ］
Amirkhani A and Barshooi A H . 2023 . DeepCar 5.0： vehicle make and model recognition under challenging conditions . IEEE Transactions on Intelligent Transportation Systems ， 24 （ 1 ）： 541 - 553 ［ DOI： 10.1109/TITS.2022.3212921 http://dx.doi.org/10.1109/TITS.2022.3212921 ］
Avianto D ， Harjoko A and Afiahayati . 2022 . CNN-based classification for highly similar vehicle model using multi-task learning . Journal of Imaging ， 8 （ 11 ）： # 293 ［ DOI： 10.3390/jimaging8110293 http://dx.doi.org/10.3390/jimaging8110293 ］
Benouini R ， Batioua I ， Zenkouar K ， Zahi A ， Najah S and Qjidaa H . 2019 . Fractional-order orthogonal Chebyshev moments and moment invariants for image representation and pattern recognition . Pattern Recognition ， 86 ： 332 - 343 ［ DOI： 10.1016/j.patcog.2018.10.001 http://dx.doi.org/10.1016/j.patcog.2018.10.001 ］
Boukerche A and Ma X R . 2022 . A novel smart lightweight visual attention model for fine-grained vehicle recognition . IEEE Transactions on Intelligent Transportation Systems ， 23 （ 8 ）： 13846 - 13862 ［ DOI： 10.1109/TITS.2021.3131530 http://dx.doi.org/10.1109/TITS.2021.3131530 ］
Chen R L ， Hawes M ， Isupova O ， Mihaylova L and Zhu H . 2017 . Online vehicle logo recognition using Cauchy prior logistic regression // Proceedings of the 20th International Conference on Information Fusion （Fusion） . Xi'an， China ： IEEE： 1 - 8 ［ DOI： 10.23919/ICIF.2017.8009720 http://dx.doi.org/10.23919/ICIF.2017.8009720 ］
Chen R L ， Hawes M ， Mihaylova L ， Xiao J J and Liu W . 2016 . Vehicle logo recognition by spatial-SIFT combined with logistic regression // Proceedings of the 19th International Conference on Information Fusion （FUSION） . Heidelberg， Germany ： IEEE： 1228 - 1235
Chen R L ， Jalal M A ， Mihaylova L and Moore R K . 2018 . Learning capsules for vehicle logo recognition // Proceedings of the 21st International Conference on Information Fusion （FUSION） . Cambridge， UK ： IEEE： 565 - 572 ［ DOI： 10.23919/ICIF.2018.8455227 http://dx.doi.org/10.23919/ICIF.2018.8455227 ］
Chhabra P ， Garg N K and Kumar M . 2020 . Content-based image retrieval system using ORB and SIFT features . Neural Computing and Applications ， 32 （ 7 ）： 2725 - 2733 ［ DOI： 10.1007/s00521-018-3677-9 http://dx.doi.org/10.1007/s00521-018-3677-9 ］
Dalal N and Triggs B . 2005 . Histograms of oriented gradients for human detection // Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition （CVPR’05） . San Diego， USA ： IEEE： 886 - 893 ［ DOI： 10.1109/CVPR.2005.177 http://dx.doi.org/10.1109/CVPR.2005.177 ］
Deng J K ， Guo J ， Xue N N and Zafeiriou S . 2019 . ArcFace： additive angular margin loss for deep face recognition // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR） . Long Beach， USA ： IEEE： 4685 - 4694 ［ DOI： 10.1109/CVPR.2019.00482 http://dx.doi.org/10.1109/CVPR.2019.00482 ］
Dewi C ， Chen A P S and Christanto H J . 2023 . YOLOv7 for face mask identification based on deep learning // Proceedings of the 15th International Conference on Computer and Automation Engineering （ICCAE） . Sydney， Australia ： IEEE： 193 - 197 ［ DOI： 10.1109/ICCAE56788.2023.10111427 http://dx.doi.org/10.1109/ICCAE56788.2023.10111427 ］
Ding S and Wu H Y . 2020 . A multi-feature fusion based vehicle logo recognition approach for traffic checkpoint . IOP Conference Series： Earth and Environmental Science ， 440 （ 2 ）： # 022071 ［ DOI： 10.1088/1755-1315/440/2/022071 http://dx.doi.org/10.1088/1755-1315/440/2/022071 ］
Dong G H and Chen X Y . 2023 . Vehicle logo recognition with YOLOv5 location and multi-feature fusion . Computer Engineering and Applications ， 59 （ 5 ）： 176 - 193
董光辉 ， 陈星宇 . 2023 . YOLOv5定位多特征融合的车标识别 . 计算机工程与应用 ， 59 （ 5 ）： 176 - 193 ［ DOI： 10.3778/j.issn.1002-8331.2207-0389 http://dx.doi.org/10.3778/j.issn.1002-8331.2207-0389 ］
Dou X Z ， Liu Y ， Lv K ， Xiong Z and Sheng H . 2021 . High confidence attribute recognition for vehicle re-identification // Proceedings of 2021 IEEE International Conference on Image Processing （ICIP） . Anchorage， USA ： IEEE： 2353 - 2357 ［ DOI： 10.1109/ICIP42928.2021.9506255 http://dx.doi.org/10.1109/ICIP42928.2021.9506255 ］
Flusser J and Suk T . 1993 . Pattern recognition by affine moment invariants . Pattern Recognition ， 26 （ 1 ）： 167 - 174 ［ DOI： 10.1016/0031-3203（93）90098-h http://dx.doi.org/10.1016/0031-3203（93）90098-h ］
Furukawa Y and Ponce J . 2010 . Accurate， dense， and robust multiview stereopsis . IEEE Transactions on Pattern Analysis and Machine Intelligence ， 32 （ 8 ）： 1362 - 1376 ［ DOI： 10.1109/tpami.2009.161 http://dx.doi.org/10.1109/tpami.2009.161 ］
Geng Q T ， Zhao H Y ， Wang Y T and Zhao H W . 2018 . A vehicle logo recognition algorithm based on the improved SIFT feature . Optics and Precision Engineering ， 26 （ 5 ）： 1267 - 1274
耿庆田 ， 赵浩宇 ， 王宇婷 ， 赵宏伟 . 2018 . 基于改进SIFT特征提取的车标识别 . 光学精密工程 ， 26 （ 5 ）： 1267 - 1274 ［ DOI： 10.3788/ope.20182605.1267 http://dx.doi.org/10.3788/ope.20182605.1267 ］
Gong X L ， Xie M L and Xu H S . 2022 . Pedestrian detection algorithm based on YOLOv3//Proceedings of the 4th International Conference on Artificial Intelligence and Advanced Manufacturing （AIAM） . Hamburg， Germany ： IEEE ： 686 - 689 ［ DOI： 10.1109/AIAM57466.2022.00139 http://dx.doi.org/10.1109/AIAM57466.2022.00139 ］
Gu Q ， Yang J Y ， Cui G L ， Kong L J ， Zheng H K and Klette R . 2016 . Multi-scale vehicle logo recognition by directional dense SIFT flow parsing // Proceedings of 2016 IEEE International Conference on Image Processing （ICIP） . Phoenix， USA ： IEEE： 3827 - 3831 ［ DOI： 10.1109/ICIP.2016.7533076 http://dx.doi.org/10.1109/ICIP.2016.7533076 ］
Gupta S ， Thakur K and Kumar M . 2021 . 2D-human face recognition using SIFT and SURF descriptors of face’s feature regions . The Visual Computer ， 37 （ 3 ）： 447 - 456 ［ DOI： 10.1007/s00371-020-01814-8 http://dx.doi.org/10.1007/s00371-020-01814-8 ］
Hassan A ， Ali M ， Durrani N M and Tahir M A . 2021 . An empirical analysis of deep learning architectures for vehicle make and model recognition . IEEE Access ， 9 ： 91487 - 91499 ［ DOI： 10.1109/ACCESS.2021.3090766 http://dx.doi.org/10.1109/ACCESS.2021.3090766 ］
He K M ， Zhang X Y ， Ren S Q and Sun J . 2016 . Identity mappings in deep residual networks // Proceedings of the 14th European Conference on Computer Vision . Amsterdam， the Netherlands ： Springer： 630 - 645 ［ DOI： 10.1007/978-3-319-46493-0_38 http://dx.doi.org/10.1007/978-3-319-46493-0_38 ］
He M X ， Yu Y and Cheng R Q . 2021 . Vehicle logo recognition method based on feature enhancement . Journal of Image and Graphics ， 26 （ 5 ）： 1030 - 1040
贺敏雪 ， 余烨 ， 程茹秋 . 2021 . 特征增强策略驱动的车标识别 . 中国图象图形学报 ， 26 （ 5 ）： 1030 - 1040 ［ DOI： 10.11834/jig.200327 http://dx.doi.org/10.11834/jig.200327 ］
He M X ， Yu Y ， Xu J T and Lu Q . 2020 . Vehicle logo recognition based on anti-blur feature extraction . Journal of Image and Graphics ， 25 （ 3 ）： 605 - 617
贺敏雪 ， 余烨 ， 徐京涛 ， 路强 . 2020 . 抗模糊特征提取策略下的车标识别 . 中国图象图形学报 ， 25 （ 3 ）： 605 - 617 ［ DOI： 10.11834/jig.190281 http://dx.doi.org/10.11834/jig.190281 ］
Howard A G ， Zhu M ， Chen B ， Kalenichenko D ， Wang W ， Weyand T ， Andreetto M and Adam H . 2017 . Mobilenets： efficient convolutional neural networks for mobile vision applications ［EB/OL］. ［ 2023-09-12 ］. http://arxiv.org/pdf/1704.04861.pdf http://arxiv.org/pdf/1704.04861.pdf
Huang H M ， Liu H S and Liu G P . 2012 . Face recognition using pyramid histogram of oriented gradients and SVM . International Journal on Advances in Information Sciences and Service Sciences ， 4 （ 18 ）： 1 - 8 ［ DOI： 10.4156/aiss.vol4.issue18.1 http://dx.doi.org/10.4156/aiss.vol4.issue18.1 ］
Huang Y ， Wu R W ， Sun Y ， Wang W and Ding X H . 2015 . Vehicle logo recognition system based on convolutional neural networks with a pretraining strategy . IEEE Transactions on Intelligent Transportation Systems ， 16 （ 4 ）： 1951 - 1960 ［ DOI： 10.1109/tits.2014.2387069 http://dx.doi.org/10.1109/tits.2014.2387069 ］
Huang Z L ， Lu X B and Chen C . 2018 . A vehicle logo recognition method based on improved SIFT feature and bag-of-words model/ /Proceedings Volume 10806 ， 10th International Conference on Digital Image Processing （ICDIP 2018）. Shanghai， China ： SPIE： 545 - 551 ［ DOI： 10.1117/12.2502972 http://dx.doi.org/10.1117/12.2502972 ］
Jamil A A ， Hussain F ， Yousaf M H ， Butt A M and Velastin S A . 2020 . Vehicle make and model recognition using bag of expressions . Sensors ， 20 （ 4 ）： # 1033 ［ DOI： 10.3390/s20041033 http://dx.doi.org/10.3390/s20041033 ］
Jiang X L ， Sun K ， Ma L Q ， Qu Z J and Ren C G . 2022 . Vehicle logo detection method based on improved YOLOv4 . Electronics ， 11 （ 20 ）： # 3400 ［ DOI： 10.3390/electronics11203400 http://dx.doi.org/10.3390/electronics11203400 ］
Jung H ， Choi M K ， Jung J ， Lee J H ， Kwon S and Jung W Y . 2017 . ResNet-based vehicle classification and localization in traffic surveillance systems // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops （CVPRW） . Honolulu， USA ： IEEE： 934 - 940 ［ DOI： 10.1109/CVPRW.2017.129 http://dx.doi.org/10.1109/CVPRW.2017.129 ］
Li N ， Xu G Z ， Lei B J ， Ma G L and Shi Y T . 2022 . Logo recognition algorithm for vehicles on traffic road . Journal of Computer Applications ， 42 （ 3 ）： 810 - 817
李讷 ， 徐光柱 ， 雷帮军 ， 马国亮 ， 石勇涛 . 2022 . 交通道路行驶车辆车标识别算法 . 计算机应用 ， 42 （ 3 ）： 810 - 817 ［ DOI： 10.11772/j.issn.1001-9081.2021040860 http://dx.doi.org/10.11772/j.issn.1001-9081.2021040860 ］
Lin Z B . 2023 . Vehicle logo recognition based on depth residual shrinkage network // Proceedings Volume 12610， 3rd International Conference on Artificial Intelligence and Computer Engineering （ICAICE 2022） . Wuhan， China ： SPIE： 749 - 753 ［ DOI： 10.1117/12.2671450 http://dx.doi.org/10.1117/12.2671450 ］
Liu Y ， Tian J W ， Hu R R ， Yang B ， Liu S ， Yin L R and Zheng W F . 2022a . Improved feature point pair purification algorithm based on SIFT during endoscope image stitching . Frontiers in Neurorobotics ， 16 ： # 840594 ［ DOI： 10.3389/fnbot.2022.840594 http://dx.doi.org/10.3389/fnbot.2022.840594 ］
Liu Z ， Mao H Z ， Wu C Y ， Feichtenhofer C ， Darrell T and Xie S N . 2022b . A ConvNet for the 2020s ［EB/OL］. ［ 2023-09-12 ］. http://arxiv.org/pdf/2201.03545.pdf http://arxiv.org/pdf/2201.03545.pdf
Llorca D F ， Arroyo R and Sotelo M A . 2013 . Vehicle logo recognition in traffic images using HOG features and SVM // Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems （ITSC 2013） . The Hague， Netherlands ： IEEE： 2229 - 2234 ［ DOI： 10.1109/ITSC.2013.6728559 http://dx.doi.org/10.1109/ITSC.2013.6728559 ］
Lowe D G . 1999 . Object recognition from local scale-invariant features // Proceedings of the 7th IEEE International Conference on Computer Vision . Kerkyra， Greece ： IEEE： 1150 - 1157 ［ DOI： 10.1109/ICCV.1999.790410 http://dx.doi.org/10.1109/ICCV.1999.790410 ］
Lowe D G . 2004 . Distinctive image features from scale-invariant keypoints . International Journal of Computer Vision ， 60 （ 2 ）： 91 - 110 ［ DOI： 10.1023/b：visi.0000029664.99615.94 http://dx.doi.org/10.1023/b：visi.0000029664.99615.94 ］
Lu L and Huang H . 2020 . Component-based feature extraction and representation schemes for vehicle make and model recognition . Neurocomputing ， 372 ： 92 - 99 ［ DOI： 10.1016/j.neucom.2019.09.049 http://dx.doi.org/10.1016/j.neucom.2019.09.049 ］
Lu W L ， Zhao H L ， He Q ， Huang H and Jin X G . 2021 . Category-consistent deep network learning for accurate vehicle logo recognition . Neurocomputing ， 463 ： 623 - 636 ［ DOI： 10.1016/j.neucom.2021.08.030 http://dx.doi.org/10.1016/j.neucom.2021.08.030 ］
Lyu Y X T ， Schiopu I ， Cornelis B and Munteanu A . 2022 . Framework for vehicle make and model recognition —— A new large-scale dataset and an efficient two-branch–two-stage deep learning architecture . Sensors ， 22 （ 21 ）： # 8439 ［ DOI： 10.3390/s22218439 http://dx.doi.org/10.3390/s22218439 ］
Ma X R and Boukerche A . 2020 . An AI-based visual attention model for vehicle make and model recognition // Proceedings of 2020 IEEE Symposium on Computers and Communications （ISCC） . Rennes， France ： IEEE： 1 - 6 ［ DOI： 10.1109/ISCC50000.2020.9219660 http://dx.doi.org/10.1109/ISCC50000.2020.9219660 ］
Peng B and Zang D . 2015 . Vehicle logo recognition based on deep learning . Computer Science ， 42 （ 4 ）： 268 - 273
彭博 ， 臧笛 . 2015 . 基于深度学习的车标识别方法研究 . 计算机科学 ， 42 （ 4 ）： 268 - 273 ［ DOI： 10.11896/j.issn.1002-137X.2015.4.055 http://dx.doi.org/10.11896/j.issn.1002-137X.2015.4.055 ］
Psyllos A ， Anagnostopoulos C N and Kayafas E . 2012 . M-SIFT： a new method for Vehicle Logo Recognition // Proceedings of 2012 IEEE International Conference on Vehicular Electronics and Safety （ICVES 2012） . Istanbul， Turkey ： IEEE： 261 - 266 ［ DOI： 10.1109/ICVES.2012.6294277 http://dx.doi.org/10.1109/ICVES.2012.6294277 ］
Qu Y C ， Zheng H C ， Chen S Y and Chen J T . 2014 . Vehicle logo recognition based on a weighted spatial pyramid framework // Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems （ITSC） . Qingdao， China ： IEEE： 1238 - 1244 ［ DOI： 10.1109/ITSC.2014.6957857 http://dx.doi.org/10.1109/ITSC.2014.6957857 ］
Redmon J ， Divvala S ， Girshick R and Farhadi A . 2016 . You only look once： unified， real-time object detection // Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition （CVPR） . Las Vegas， USA ： IEEE： 779 - 788 ［ DOI： 10.1109/CVPR.2016.91 http://dx.doi.org/10.1109/CVPR.2016.91 ］
Sandler M ， Howard A ， Zhu M L ， Zhmoginov A and Chen L C . 2019 . MobileNetV2： inverted residuals and linear bottlenecks ［EB/OL］. ［ 2023-09-12 ］. http://arxiv.org/pdf/1801.04381.pdf http://arxiv.org/pdf/1801.04381.pdf
Shi X H ， Ma S L ， Shen Y ， Yang Y K and Tan Z X . 2023 . Vehicle logo detection using an IoAverage loss on dataset VLD100K-61 . EURASIP Journal on Image and Video Processing ， 2023 （ 1 ）： # 4 ［ DOI： 10.1186/s13640-023-00604-1 http://dx.doi.org/10.1186/s13640-023-00604-1 ］
Simonyan K and Zisserman A . 2015 . Very deep convolutional networks for large-scale image recognition ［EB/OL］. ［ 2023-09-12 ］. http://arxiv.org/pdf/1409.1556.pdf http://arxiv.org/pdf/1409.1556.pdf
Soon F C ， Khaw H Y and Chuah J H . 2015 . Pattern recognition of vehicle logo using Tchebichef and Legendre moment // Proceedings of 2015 IEEE Student Conference on Research and Development （SCOReD） . Kuala Lumpur， Malaysia ： IEEE： 82 - 86 ［ DOI： 10.1109/SCORED.2015.7449438 http://dx.doi.org/10.1109/SCORED.2015.7449438 ］
Soon F C ， Khaw H Y ， Chuah J H and Kanesan J . 2018 . Hyper parameters optimisation of deep CNN architecture for vehicle logo recognition . IET Intelligent Transport Systems ， 12 （ 8 ）： 939 - 946 ［ DOI： 10.1049/iet-its.2018.5127 http://dx.doi.org/10.1049/iet-its.2018.5127 ］
Surwase S and Pawar M . 2023 . Multi-scale multi-stream deep network for car logo recognition . Evolutionary Intelligence ， 16 （ 2 ）： 485 - 492 ［ DOI： 10.1007/s12065-021-00671-1 http://dx.doi.org/10.1007/s12065-021-00671-1 ］
Szegedy C ， Liu W ， Jia Y Q ， Sermanet P ， Reed S ， Anguelov D ， Erhan D ， Vanhoucke V and Rabinovich A . 2014 . Going deeper with convolutions ［EB/OL］. ［ 2023-09-12 ］. http://arxiv.org/pdf/1409.4842.pdf http://arxiv.org/pdf/1409.4842.pdf
Tian Q and Jia X N . 2021 . Vehicle logo recognition based on deep residual network . Journal of Jilin University （Science Edition） ， 59 （ 2 ）： 319 - 324
田强 ， 贾小宁 . 2021 . 基于深度残差网络的车标识别 . 吉林大学学报（理学版） ， 59 （ 2 ）： 319 - 324 ［ DOI： 10.13413/j.cnki.jdxblxb.2020001 http://dx.doi.org/10.13413/j.cnki.jdxblxb.2020001 ］
Wang D ， Al-Rubaie A ， Alsarkal Y I ， Stincic S and Davies J . 2021 . Cost effective and accurate vehicle make/model recognition method using YoloV5//Proceedings of 2021 International Conference on Smart Applications， Communications and Networking （SmartNets） . Glasgow， UK ： IEEE ： 1 - 4 ［ DOI： 10.1109/SmartNets50376.2021.9555409 http://dx.doi.org/10.1109/SmartNets50376.2021.9555409 ］
Wang Y R ， Zhu X L and Wu B . 2019 . Automatic detection of individual oil palm trees from UAV images using HOG features and an SVM classifier . International Journal of Remote Sensing ， 40 （ 19 ）： 7356 - 7370 ［ DOI： 10.1080/01431161.2018.1513669 http://dx.doi.org/10.1080/01431161.2018.1513669 ］
Xia Y Z ， Feng J and Zhang B L . 2016 . Vehicle logo recognition and attributes prediction by multi-task learning with CNN // Proceedings of the 12th International Conference on Natural Computation， Fuzzy Systems and Knowledge Discovery （ICNC-FSKD） . Changsha， China ： IEEE： 668 - 672 ［ DOI： 10.1109/FSKD.2016.7603254 http://dx.doi.org/10.1109/FSKD.2016.7603254 ］
Xiang Z Q ， Zou Y X ， Zhou X Q and Huang X L . 2016 . Robust vehicle logo recognition based on locally collaborative representation with principal components // Proceedings of the 6th International Conference on Information Science and Technology （ICIST） . Dalian， China ： IEEE： 487 - 491 ［ DOI： 10.1109/ICIST.2016.7483463 http://dx.doi.org/10.1109/ICIST.2016.7483463 ］
Yang G F ， Wang H ， Huang M ， Wang Y M and Ma Y Q . 2013 . Research and implementation of vehicle-logo recognition based on modified invariant moments . Journal of Zhengzhou University of Light Industry （Natural Science） ， 28 （ 2 ）： 91 - 94
杨国锋 ， 王欢 ， 黄敏 ， 王艳明 ， 马亚琼 . 2013 . 基于修正不变矩的车标识别研究与实现 . 郑州轻工业学院学报（自然科学版） ， 28 （ 2 ）： 91 - 94 ［ DOI： 10.3969/j.issn.2095-476X.2013.02.021 http://dx.doi.org/10.3969/j.issn.2095-476X.2013.02.021 ］
Yang S ， Bo C J ， Zhang J X ， Gao P X ， Li Y J and Serikawa S . 2022 . VLD-45： a big dataset for vehicle logo recognition and detection . IEEE Transactions on Intelligent Transportation Systems ， 23 （ 12 ）： 25567 - 25573 ［ DOI： 10.1109/tits.2021.3062113 http://dx.doi.org/10.1109/tits.2021.3062113 ］
Yin K N ， Hou S Q ， Li Y ， Li C and Yin G Q . 2020 . A real-time vehicle logo detection method based on improved YOLOv2//Proceedings of the 15th International Conference on Wireless Algorithms， Systems， and Applications . Qingdao， China ： Springer ： 666 - 677 ［ DOI： 10.1007/978-3-030-59016-1_55 http://dx.doi.org/10.1007/978-3-030-59016-1_55 ］
Yu S Y ， Zheng S B ， Yang H and Liang L F . 2013 . Vehicle logo recognition based on Bag-of-Words // Proceedings of the 10th IEEE International Conference on Advanced Video and Signal Based Surveillance . Krakow， Poland ： IEEE： 353 - 358 ［ DOI： 10.1109/AVSS.2013.6636665 http://dx.doi.org/10.1109/AVSS.2013.6636665 ］
Yu Y ， Li H ， Wang J ， Min H ， Jia W ， Yu J and Chen C W . 2021a . A multilayer pyramid network based on learning for vehicle logo recognition . IEEE Transactions on Intelligent Transportation Systems ， 22 （ 5 ）： 3123 - 3134 ［ DOI： 10.1109/TITS.2020.2981737 http://dx.doi.org/10.1109/TITS.2020.2981737 ］
Yu Y ， Wang J ， Lu J T ， Xie Y and Nie Z X . 2018 . Vehicle logo recognition based on overlapping enhanced patterns of oriented edge magnitudes . Computers and Electrical Engineering ， 71 ： 273 - 283 ［ DOI： 10.1016/j.compeleceng.2018.07.045 http://dx.doi.org/10.1016/j.compeleceng.2018.07.045 ］
Yu Y ， Xu J T ， He M X and Lu Q . 2019 . Vehicle logo recognition based on local quantization of enhanced edge gradient features . Journal of Image and Graphics ， 24 （ 9 ）： 1458 - 1471
余烨 ， 徐京涛 ， 贺敏雪 ， 路强 . 2019 . 增强边缘梯度特征局部量化策略驱动下的车标识别 . 中国图象图形学报 ， 24 （ 9 ）： 1458 - 1471 ［ DOI： 10.11834/jig.180638 http://dx.doi.org/10.11834/jig.180638 ］
Yu Y T ， Guan H Y ， Li D L and Yu C H . 2021b . A cascaded deep convolutional network for vehicle logo recognition from frontal and rear images of vehicles . IEEE Transactions on Intelligent Transportation Systems ， 22 （ 2 ）： 758 - 771 ［ DOI： 10.1109/TITS.2019.2956082 http://dx.doi.org/10.1109/TITS.2019.2956082 ］
Zhang S F ， Wen L Y ， Bian X ， Lei Z and Li S Z . 2018a . Single-shot refinement neural network for object detection // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City， USA ： IEEE： 4203 - 4212 ［ DOI： 10.1109/CVPR.2018.00442 http://dx.doi.org/10.1109/CVPR.2018.00442 ］
Zhang H ， Dana K ， Shi J P ， Zhang Z Y ， Wang X G ， Tyagi A and Agrawal A . 2018b . Context encoding for semantic segmentation // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City， USA ： IEEE： 7151 - 7160 ［ DOI： 10.1109/CVPR.2018.00747 http://dx.doi.org/10.1109/CVPR.2018.00747 ］
Zhang J ， Wen T ， He T ， Wang X Z ， Hao R Q ， Liu J X ， Du X H and Liu L . 2022 . Human stools classification for gastrointestinal health based on an improved ResNet18 model with dual attention mechanism // Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops （CVPRW） . New Orleans， USA ： IEEE： 2095 - 2102 ［ DOI： 10.1109/CVPRW56347.2022.00227 http://dx.doi.org/10.1109/CVPRW56347.2022.00227 ］
Zhang J X ， Chen L J ， Bo C J and Yang S . 2021a . Multi-scale vehicle logo detector . Mobile Networks and Applications ， 26 （ 1 ）： 67 - 76 ［ DOI： 10.1007/s11036-020-01722-0 http://dx.doi.org/10.1007/s11036-020-01722-0 ］
Zhang J X ， Yang S ， Bo C J and Zhang Z Y . 2021b . Vehicle logo detection based on deep convolutional networks . Computers and Electrical Engineering ， 90 ： # 107004 ［ DOI： 10.1016/j.compeleceng.2021.107004 http://dx.doi.org/10.1016/j.compeleceng.2021.107004 ］
Zhang L ， Zhang D M and Zheng H . 2016 . Vehicle logo recognition using convolutional neural network combined with multiple layer feature . Journal of Computer Applications ， 36 （ 2 ）： 444 - 448
张力 ， 张洞明 ， 郑宏 . 2016 . 基于联合层特征的卷积神经网络在车标识别中的应用 . 计算机应用 ， 36 （ 2 ）： 444 - 448 ［ DOI： 10.11772/j.issn.1001-9081.2016.02.0444 http://dx.doi.org/10.11772/j.issn.1001-9081.2016.02.0444 ］
Zhao J D and Wang X K . 2019 . Vehicle-logo recognition based on modified HU invariant moments and SVM . Multimedia Tools and Applications ， 78 （ 1 ）： 75 - 97 ［ DOI： 10.1007/s11042-017-5254-0 http://dx.doi.org/10.1007/s11042-017-5254-0 ］
Zhao Q and Guo W H . 2022 . Detection of logos of moving vehicles under complex lighting conditions . Applied Sciences ， 12 （ 8 ）： # 3835 ［ DOI： 10.3390/app12083835 http://dx.doi.org/10.3390/app12083835 ］
Zhu L ， Yu F R ， Wang Y G ， Ning B and Tang T . 2019 . Big data analytics in intelligent transportation systems： a survey . IEEE Transactions on Intelligent Transportation Systems ， 20 （ 1 ）： 383 - 398 ［ DOI： 10.1109/TITS.2018.2815678 http://dx.doi.org/10.1109/TITS.2018.2815678 ］
Zhu W J ， Chen Y H ， Feng Y J ， Wang J and Yu Y . 2019 . A vehicle logo recognition method based on objective optimization . Journal of Graphics ， 40 （ 4 ）： 689 - 696
朱文佳 ， 陈宇红 ， 冯瑜瑾 ， 王俊 ， 余烨 . 2019 . 一种基于目标优化学习的车标识别方法 . 图学学报 ， 40 （ 4 ）： 689 - 696
相关作者
相关机构
京公网安备11010802024621
