Traffic Sign Detection and Recognition Using Single Shot Multibox Detector

Authors

DOI:

https://doi.org/10.18372/1990-5548.67.15582

Abstract

The article is devoted to the automatic detection and recognition of traffic signs system design which processes the images received from the vehicle digital video recorder. The digital video recorder is used both for its intended purpose and to include it in the process of driving, facilitating the driver's work and, thus, significantly increasing safety and driving comfort. The analysis of the solution of the problem of detection and recognition of traffic signs based on the use of convolutional neural networks is carried out. It is shown that the greatest advantage from the point of view of the criteria of accuracy and speed of response has the single shot multibox detector method. The learning of neural network is done based on traffic signs (adopted in Ukraine) learning sample The study showed that the proposed approach for all used datasets gave both the best recognition quality and maximum performance.

Author Biographies

Olena Chumachenko, National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”

Technical Cybernetic Department

Doctor of Engineering Science. Professor

Vladysav Konchinsky , National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”

Technical Cybernetic Department

Bachelor

References

https://preview.thenewsmarket.com/Previews /NCAP /DocumentAssets/188042.pdf

https://media.daimler.com/marsMediaSite/en /instance/ko/Speed-Limit-Assist-Electronic-image-processing-system-detects-speed-limit-signs-as-the-car-drives-past-them.xhtml?oid=9361557

http://volvo.custhelp.com/app/answers/detail /a_id /9572/~/road-sign-information %28rsi%29#:~: text=Road%20Sign%20Information%20(RSI)%20automatically,road%20you%20are%20traveling%20on

A. Gudigar, S. Chokkadi and U. Raghavendra, Multimedia Tools and Applications. (2016) 75: 333. Available at: https://doi.org/10.1007/s11042-014-2293-7.

S. Escalera, X. Barу, O. Pujol, J. Vitriа, and P. Radeva, “Background on Traffic Sign Detection and Recognition.Traffic-Sign Recognition Systems,” Springer Briefs in Computer Science, 2011, pp. 5–13. https://doi.org/10.1007/978-1-4471-2245-6_2

Zumra Malik and Imran Siddiqi, “Detection and Recognition of Traffic Signs from Road Scene Image,” 12th IEEE International Conference on Frontiers of Information Technology, 2014, pp. 330–335. https://doi.org/10.1109/FIT.2014.68

C. F. Paulo and P. L. Correia, “Automatic Detection and Classification of Traffic Signs in Image Analysis for Multimedia Interactive Services (WIAMIS '07),” Eighth International Workshop on. 6–8 June 2007, pp. 1–11. https://doi.org/10.1109/WIAMIS.2007.24

R. Timofte, K. Zimmermann and L. Van Gool, Machine Vision and Applications, 2014, no. 25, pp. 633–647. https://doi.org/10.1007/s00138-011-0391-3

C. Z. Xiong, C. Wang, W. X. Ma, and Y. M. Shan, “A Traffic Sign Detection Algorithm Based on Deep Convolutional Neural Network,” in Proceedings of the IEEE International Conference on Signal and Image Processing, Beijing, China, 13–15 August, 2016, pp. 676–679.

D. Ciresan, U. Meier, J. Masci and J. Schmidhuber, “Multi-Column Deep Neural Network for Traffic Sign Classification,” Neural Networks, vol. 32, pp. 333–338, 2012. https://doi.org/10.1016/j.neunet.2012.02.023

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, 521(7553), pp. 436–444, 2015. https://doi.org/10.1038/nature14539

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems, 2012, pp. 1097–1105.

A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar and L. Fei-Fei, “Large-scale video classification with convolutional neural networks,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, 2014, pp. 1725–1732. https://doi.org/10.1109/CVPR.2014.223

B. Zhou, A. Lapedriza, J. Xiao, A. Torralba and A. Oliva, “Learning deep features for scene recognition using places database,” in Advances in neural information processing systems, 2014, pp. 487–495.

J. Jin, K. Fu, and C. Zhang, “Traffic Sign Recognition with Hinge Loss Trained Convolution Neural Networks,” IEEE Trans. Intell. Transp. Syst. 2014, 15:1991–2000. https://doi.org/10.1109/TITS.2014.2308281

T. Y.-H. Chen, L. Ravindranath, S. Deng, P. Bahl and H. Balakrishnan, “Glimpse: Continuous, real-time object recognition on mobile devices,” in Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, 2015, pp. 155–168.

R. Girshick, “Rich feature hierarchies for accurate object detection and semantic Segmentation,” arXiv.org [Electronic resource]. 2013, URL: https://arxiv.org/abs/1311.2524 (date of the application: 12.03.2018).

R. Girshick, Fast R-CNN, arXiv.org [Electronic resource]. 2015. – URL:https://arxiv.org/abs/1504.08083 (date of the application: 13.03.2018). https://doi.org/10.1109/ICCV.2015.169

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” Proceedings of the Neural Information Processing Systems conference, NIPS, 2015.

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” Computer Vision and Pattern Recognition, CVPR, 2016. https://doi.org/10.1109/CVPR.2016.91

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, and S. E. Reed, “SSD: single shot multibox detector,” CoRR, 2015. https://doi.org/10.1007/978-3-319-46448-0_2

D. Y. Erokhin and M. D. Ershov, “Modern Convolutional Neural Networks for Object Detection and Recognition,” Digital Signal Processing, no. 3, pp. 64–69, 2018.

S. Wan, Z. Chen, T. Zhang, B. Zhang, and K. Wong, “Bootstrapping Face Detection with Hard Negative Examples,” arXiv:1608.02236. 2016, 7 p.

Downloads

Published

2021-05-12

Issue

Section

COMPUTER SCIENCES AND INFORMATION TECHNOLOGIES