全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

交通标志检测与分类方法综述
A Survey of Traffic Sign Detection and Classification Methods

DOI: 10.12677/SEA.2021.103039, PP. 348-353

Keywords: 交通标志检测,分类,综述,手工特征,深度学习
Traffic Sign Detection
, Classification, Overview, Manual Feature, Deep Learning

Full-Text   Cite this paper   Add to My Lib

Abstract:

在过去的几年中,很多交通标志检测和分类方法被提出。本文综述了近年来交通标志检测与分类的一些有效方法。检测的主要目标是定位包含交通标志的感兴趣区域,检测方法大致分为三大类:基于颜色、基于形状和基于学习。分类方法主要分为两类:基于手工特征的学习方法和深度学习方法。为了便于参考,还将检测和分类公开数据集进行了总结。
In the past few years, many traffic sign detection and classification methods have been proposed. This paper summarizes some effective methods of traffic sign detection and classification in recent years. The main goal of detection is to locate the region of interest containing traffic signs. Detection methods are roughly divided into three categories: color based, shape based and learning based. Classification methods are mainly divided into two categories: manual feature-based learning method and deep learning method. For reference, different detection and classification methods and different data sets are summarized.

References

[1]  谌华, 郭伟, 闫敬文, 等. 基于深度学习的SAR图像道路识别新方法[J]. 吉林大学学报(工学版), 2020, 50(5): 1778-1787.
[2]  李锦泽. 基于深度学习的行人检测技术研究[D]: [硕士学位论文]. 北京: 中国人民公安大学, 2020.
[3]  邓淇天, 李旭. 基于多特征融合的车辆检测算法[J]. 传感器与微系统, 2020, 39(6): 136-139.
[4]  高铭悦, 董全德. 基于深度学习的交通标志识别技术研究[J]. 兰州文理学院学报(自然科学版), 2020, 34(3): 98-102.
[5]  徐先峰, 郎彬, 张丽, 等. 基于R分量的交通标志ROI提取[J]. 计算机与数字工程, 2019, 47(4): 919-923.
[6]  吕凯凯, 韦德泉, 王猛. 基于HSB颜色空间的交通标志识别研究[J]. 赤峰学院学报(自然科学版), 2019, 35(5): 42-44.
[7]  刘祥. 基于区域颜色分割的交通标志检测和识别探析[J]. 科学与信息化, 2019(6): 139.
[8]  苗丹, 卢伟, 高娇娇, 等. 基于聚类与Hough变换的交通标志检测方法[J]. 计算机系统应用, 2019, 28(11): 213-217.
[9]  崔利娟. 基于深度森林的交通标志识别算法研究[D]: [硕士学位论文]. 北京: 北方工业大学, 2019.
[10]  张木易. 复杂背景下交通标志的检测与识别研究[D]: [硕士学位论文]. 西安: 西安电子科技大学, 2020.
[11]  黄尚安. 基于卷积神经网络的交通标志检测和识别算法研究[D]: [硕士学位论文]. 江门: 五邑大学, 2019.
[12]  Yildiz, G. and Dizdarolu, B. (2019) Renk ve ekle Dayal Yaklamla Trafik areti Saptama/Traffic Sign Detection via Color and Shape-Based Approach. 1st International Informatics and Software Engineering Conference (UBMYK), Ankara, 6-7 November 2019, 1-5.
https://doi.org/10.1109/UBMYK48245.2019.8965590
[13]  于平平, 齐林, 马苗立, 等. 基于视觉注意机制和形状特征的交通标志检测方法[J]. 数学的实践与认识, 2019, 49(21): 125-133.
[14]  Viola, P.A. and Jones, M.J. (2001) Rapid Object Detection Using a Boosted Cascade of Simple Features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, 8-14 December 2001 1-9.
[15]  Brkic, K. and Pinz, A. (2009) Traffic Sign Detection as a Component of an Automated Traffic Infrastructure Inventory System.
[16]  Baro, X., Escalera, S., Vitria, J., et al. (2009) Traffic Sign Recognition Using Evolutionary AdaBoost Detection and Forest-ECOC Classification. IEEE Transactions on Intelligent Transportation Systems, 10, 113-126.
https://doi.org/10.1109/TITS.2008.2011702
[17]  Di, Z., Zhang, J., Zhang, D., et al. (2016) Traffic Sign Detection Based on Cascaded Convolutional Neural Networks. 2016 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), Shanghai, 30 May-1 June 2016, 201-206.
https://doi.org/10.1109/SNPD.2016.7515901
[18]  Zaklouta, F., Stanciulescu, B. and Hamdoun, O. (2011) Traffic Sign Classification Using K-d Trees and Random Forests. The 2011 International Joint Conference on Neural Networks (IJCNN), Dallas, 4-9 August 2013, 2151-2155.
https://doi.org/10.1109/IJCNN.2011.6033494
[19]  Stallkamp, J., Schlipsing, M., Salmen, J., et al. (2012) Man vs. Computer: Benchmarking Machine Learning Algorithms for Traffic Sign Recognition. Neural Networks, 32, 323-332.
https://doi.org/10.1016/j.neunet.2012.02.016
[20]  Abedin, M.Z., Dhar, P. and Deb, K. (2016) Traffic Sign Recognition Using SURF: Speeded Up Robust Feature Descriptor and Artificial Neural Network Classifier. IEEE International Conference on Electrical & Computer Engineering, Dhaka, 20-22 December 2016, 198-201.
https://doi.org/10.1109/ICECE.2016.7853890
[21]  Chung, J.H., Dong, W.K., Kang, T.K., et al. (2020) Traffic Sign Recognition in Harsh Environment Using Attention Based Convolutional Pooling Neural Network. Neural Processing Letters, 51, 2551-2573.
https://doi.org/10.1007/s11063-020-10211-0
[22]  Jin, Y., Fu, Y., Wang, W., et al. (2020) Multi-Feature Fusion and Enhancement Single Shot Detector for Traffic Sign Recognition. IEEE Access, 8, 38931-38940.
https://doi.org/10.1109/ACCESS.2020.2975828
[23]  李超, 杨艳. 基于改进网中网神经网络的交通标志识别[J]. 信息技术, 2019(9): 137-140.
[24]  Novak, B., Ilic, V. and Pavkovic, B. (2020) YOLOv3 Algorithm with Additional Convolutional Neural Network Trained for Traffic Sign Recognition. Zooming Innovation in Consumer Technologies Conference (ZINC), Novi Sad, 26-27 May 2020, 165-168.
https://doi.org/10.1109/ZINC50678.2020.9161446
[25]  Ahmed, S., Kamal, U. and Hasan, M.K. (2020) DFR-TSD: A Deep Learning Based Framework for Robust Traffic Sign Detection under Challenging Weather Conditions. IEEE Transactions on Intelligent Transportation Systems, 1-13.
https://doi.org/10.1109/TITS.2020.3048878
[26]  Yla, D., Jp, A., Jhx, B., et al. (2021) TSingNet: Scale-Aware and Context-Rich Feature Learning for Traffic Sign Detection and Recognition in the Wild. Neurocomputing, 447, 10-22.
https://doi.org/10.1016/j.neucom.2021.03.049

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133