Traffic signs detection and recognition under low-illumination conditions

ZHAO Kun; LIU Li; MENG Yu; SUN Ruo-can

doi:10.13374/j.issn2095-9389.2019.08.14.003

Volume 42 Issue 8

Aug. 2020

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Engineering > 2020 > 42(8): 1074-1084

ZHAO Kun, LIU Li, MENG Yu, SUN Ruo-can. Traffic signs detection and recognition under low-illumination conditions[J]. Chinese Journal of Engineering, 2020, 42(8): 1074-1084. doi: 10.13374/j.issn2095-9389.2019.08.14.003

Citation:

ZHAO Kun, LIU Li, MENG Yu, SUN Ruo-can. Traffic signs detection and recognition under low-illumination conditions[J]. Chinese Journal of Engineering, 2020, 42(8): 1074-1084. doi: 10.13374/j.issn2095-9389.2019.08.14.003

Citation:

PDF( 2118 KB)

Traffic signs detection and recognition under low-illumination conditions

doi: 10.13374/j.issn2095-9389.2019.08.14.003

School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China

More Information

Corresponding author: E-mail: myu@ustb.edu.cn
Received Date: 2019-08-14
Publish Date: 2020-09-11

Abstract

Abstract

Traffic sign detection and recognition, which are important to ensure traffic safety, have been a research hotspot. In recent years, with the rapid development of automated driving technology, significant progress has been made in developing more accurate and efficient deep learning algorithms for traffic sign detection and recognition. However, these studies mainly focus on foreign traffic signs and do not consider the low-illumination conditions in practical application, which is a common scene. Therefore, many challenges still exist in the application of traffic sign detection and recognition in traffic scenes. To solve the problems of easy omission and inaccurate positioning for traffic sign detection and recognition under complex illumination conditions, the enhanced YOLOv3 (You only look once) detection algorithm, a traffic sign detection and recognition method combining real-time adaptive image enhancement and the YOLOv3 frame was proposed. First, a large and complex illumination traffic sign dataset for Chinese traffic was constructed; it included globally low illumination, locally low illumination, and sufficient illumination images. Then an adaptive enhancement algorithm was proposed for low-illumination images, which can enhance the difference between traffic signs and background by adjusting the brightness and contrast of the images. Finally, high-quality and discrimination images as input were transmitted to the YOLOv3 network framework, and traffic sign detection and recognition were performed. To reduce the influences of the prior anchor box setting accuracy and the imbalance between the background and foreground on the detection accuracy, the clustering algorithm for the prior anchor box and loss function for the network were optimized. The results of the comparison experiment with the LISA dataset and complex illumination traffic sign dataset for Chinese traffic show that the proposed enhanced YOLOv3 detection algorithm has higher regression accuracy and category confidence than the published YOLOv3 algorithm for traffic signs; the recall and precision are higher by 0.96% and 0.48%, respectively, which indicates the application potential of the proposed algorithm in actual traffic scenarios.
- traffic sign detection,
- low illumination,
- adaptive image enhancement,
- YOLOv3,
- deep learning

FullText(HTML)

References(26)

References

[1]	張新鈺, 高洪波, 趙建輝, 等. 基于深度學習的自動駕駛技術綜述. 清華大學學報: 自然科學版, 2018, 58(4):438 Zhang X Y, Gao H B, Zhao J H, et al. Overview of deep learning intelligent driving methods. <italic>J Tsinghua Univ Sci Technol</italic>, 2018, 58(4): 438
[2]	Salti S, Petrelli A, Tombari F, et al. Traffic sign detection via interest region extraction. <italic>Pattern Recognit</italic>, 2015, 48(4): 1039 doi: 10.1016/j.patcog.2014.05.017
[3]	Eichner M L, Breckon T P. Integrated speed limit detection and recognition from real-time video // 2008 IEEE Intelligent Vehicles Symposium. Eindhoven, 2008: 626
[4]	Lorsakul A, Suthakorn J. Traffic sign recognition using neural network on opencv: toward intelligent vehicle/driver assistance system. <italic>Intell Service Rob</italic>, 2007: 1
[5]	Barnes N, Zelinsky A. Real-time radial symmetry for speed sign detection // IEEE Intelligent Vehicles Symposium. Parma, 2004: 566
[6]	Loy G, Barnes N. Fast shape-based road sign detection for a driver assistance system // 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Sendai, 2004: 70
[7]	Maldonado-Bascon S, Lafuente-Arroyo S, Gil-Jimenez P, et al. Road-sign detection and recognition based on support vector machines. <italic>IEEE Trans Intell Transportation Syst</italic>, 2007, 8(2): 264 doi: 10.1109/TITS.2007.895311
[8]	Bahlmann C, Zhu Y, Ramesh V, et al. A system for traffic sign detection, tracking, and recognition using color, shape, and motion information // IEEE Proceedings. Intelligent Vehicles Symposium. Las Vegas, 2005: 255
[9]	Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation // 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, 2014: 580
[10]	Uijlings J R R, van de Sande K E A, Gevers T, et al. Selective search for object recognition. <italic>Int J Comput Vision</italic>, 2013, 104(2): 154 doi: 10.1007/s11263-013-0620-5
[11]	He K M, Zhang X Y, Ren S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition. <italic>IEEE Transactions Pattern Anal Mach Intell</italic>, 2015, 37(9): 1904 doi: 10.1109/TPAMI.2015.2389824
[12]	Girshick R. Fast R-CNN // 2015 IEEE International Conference on Computer Vision (ICCV). Santiago, 2015: 1440
[13]	Ren S Q, He K M, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks. <italic>IEEE Transactions Pattern Anal Mach Intell</italic>, 2017, 39(6): 1137 doi: 10.1109/TPAMI.2016.2577031
[14]	Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection // 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, 2016: 779
[15]	Liu W, Anguelov D, Erhan D, et al. SSD: Single shot multibox detector // European Conference on Computer Vision. Amsterdam, 2016: 21
[16]	Ciresan D, Meier U, Masci J, et al. A committee of neural networks for traffic sign classification // The 2011 International Joint Conference on Neural Networks. San Jose, 2011: 1918
[17]	Sermanet P, LeCun Y. Traffic sign recognition with multi-scale convolutional networks // The 2011 International Joint Conference on Neural Networks. San Jose, 2011: 2809
[18]	Ciresan D, Meier U, Masci J, et al. Multi-column deep neural network for traffic sign classification. <italic>Neural Networks</italic>, 2012, 32: 333 doi: 10.1016/j.neunet.2012.02.023
[19]	Jin J Q, Fu K, Zhang C S. Traffic sign recognition with hinge loss trained convolutional neural networks. <italic>IEEE Trans-actions Intell Transportation Syst</italic>, 2014, 15(5): 1991 doi: 10.1109/TITS.2014.2308281
[20]	Mogelmose A, Trivedi M M, Moeslund T B. Vision-based traffic sign detection and analysis for intelligent driver assis-tance systems: perspectives and survey. <italic>IEEE Transactions Intell Transportation Syst</italic>, 2012, 13(4): 1484 doi: 10.1109/TITS.2012.2209421
[21]	Stallkamp J, Schlipsing M, Salmen J, et al. Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition. <italic>Neural Networks</italic>, 2012, 32: 323 doi: 10.1016/j.neunet.2012.02.016
[22]	Timofte R, Zimmermann K, Gool L V. Multi-view traffic sign detection, recognition, and 3D localization. <italic>Mach Vision Appl</italic>, 2014, 25(3): 633 doi: 10.1007/s00138-011-0391-3
[23]	余超超. 交通標志檢測與識別算法研究[學位論文]. 成都: 西南交通大學, 2017 Yu C C. Research on Traffic Sign Detection and Recognition Algorithm[Dissertation]. Chengdu: Southwest Jiaotong University, 2017
[24]	Celik T, Tjahjadi T. Automatic image equalization and contrast enhancement using Gaussian mixture modeling. <italic>IEEE Trans Image Process</italic>, 2011, 21(1): 145
[25]	Redmon J, Farhadi A. YOLOv3: an Incremental Improvement[J/OL]. arXiv preprint (2018-04-08)[2019-08-14]. http://arxiv.org/abs/1804.02767
[26]	Redmon J, Farhadi A. YOLO9000: Better, faster, stronger // 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, 2017: 6517