Detection of Traffic Sign Using CNN

Author: Simran, Sristi Tandon, Shilpi Khanna Recent Trends inParallel Computing-STM Journals Issn: 2393-8749 Date: 2023-05-02 01:38 Volume: 9 Issue: 1 Keyworde: Detection, classification, CNN, datasets, deep learning Full Text PDF Submit Manuscript Journals


For past more than half of the decade, the detection and recognition of the traffic signs are an active research area. Specially in the field of automation of driving that is critical for driverless driving, as there is drastic increase in road accidents due to ignorance of traffic signs and rules. This is frequently used for recognizing permanent or temporary various road signs which are displayed on the side of every small and long road. A complete recognition system may consist of detection of traffic sign as well as their recognition. The detection of traffic sign as well as their recognition is typically used on portable devices. This helps the devices to make better decisions and improve their driving algorithms. The preferable parameters that need to be considered are size of the traffic sign boards and speed of the vehicles. This paper addresses the real time detection and recognition of traffic sign from the traffic sign boards through convolutional neural networks. Furthermore, image segmentation techniques such as edge-based segmentation, region-based segmentation, cluster- based segmentation, and so on play an important role in traffic sign detection and recognition. Furthermore, convolutional neural networks lead to a robust system that achieves higher accuracy in detection phase as well as higher performance ability in training and recognition phase. The proposed model achieves recognition accuracy up to 98.13% at the training.

Keyworde: Detection, classification, CNN, datasets, deep learning

Full Text PDF


  1. Chang Failing, Huang Cui, and Liu Chengdu, Traffic sign detection based on Gaussian colour model and SVM, Chinese Journal of Scientific Instrument, 2014, 35 (1): 43–49.
  2. Ge Xia, Yu Feng in, and Chen Ying, Ransack Algorithm with Harris angular point for detecting traffic signs, Transducer and Microsystem Technologies, 2017, 36 (3): 124–127.
  3. Liu Hansen, Zhao Xiangmo, and Li Qian, Traffic sign recognition method based on graphical model and convolutional neural network, Journal of Traffic and Transportation Engineering, 2016, (3): 124–127.
  4. Wang Xiaoping, Huang Janie, and Liu Wensum. Traffic sign recognition based on optimized convolutional neural network architecture, Jamal of Computer Applications, 2017, 37 (2): 530– 534.
  5. Xin Jin, Cain Fixing, and Deng Haiti, Traffic Sign Classification Based on Deep Learning of Image Invariant Feature, Journal of Computer-Aided Design & Computer Graphics, 2017, 29 (4): 632–640.
  6. Radhey Shyam, Convolutional Neural Network and its Architectures, Journal of Computer Technology & Applications. 2021; 12 (2): 6–14p.
  7. Srivastava Vartika and Shyam Radhey, Enhanced object detection with deep convolutional neural networks, International Journal of all Research Education and Scientific Methods, 9(7), 2021.
  8. Zhou Zhuhai, Machine learning. Beijing: Tsinghua University Press, 2016: 23-47.
  9. Radhey Shyam and Ria Singh, A Taxonomy of Machine Learning Techniques, Journal of Advancements in Robotic’s. 2021; 8 (3): 18–25p.
  10. Radhey Shyam and Riya Chakraborty, Machine Learning and its Dominant Paradigms, Journal of Advancements in Robotics. 2021; 8 (2): 1–10p.
  11. Radhey Shyam and Gautami Awasthi. Role of Deep Learning in Image Recognition. Journal of Image Processing & Pattern Recognition Progress. 2021; 8 (2): 34–39p.
  12. Ren S, He K, and Airsick R, Faster R-CNN: towards realtime object detection with region proposal networks, International Conference on Neural Information Processing Systems. MIT Press, 2015, 91–99. Detection of Traffic Sign Using CNN Simran et al. © STM Journals 2022. All Rights Reserved 23
  13. Krizhevsky A, Sutskever I, and Hinton GE, Image Net classification with deep convolutional neural networks, International Conference on Neural Information Processing Systems, 2012, 1097–1105.
  14. Zhimin xu, Si Zuo, AutoSegNet: An Automated Neural Network for Image Segmentation, IEEE Access, 2020, 92452–92461
  15. Hoban S, Stall Kamp J, and Salman J, Detection of traffic signs in real-world images: The German traffic sign detection benchmark, International Joint Conference on Neural Networks, IEEE, 2013, 1–8.
  16. B. Zoph and Q.V. Le, Neural architecture search with rein-forcement learning, 2016.
  17. B. Baker, O. Gupta, N. Naik, and R. Raskar, Designing neural network architectures using reinforcement learning, 2016.
  18. O. Ronneberger, P. Fischer, and T. Brox, U-Net: Convolutional networks for biomedical image segmentation, in Lect. Notes Comput. Sci., 2015, pp. 234–241.
  19. L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs, IEEE Trans. Pattern Anal. Mach. Intell., vol. 40, no. 4, pp. 834–848, Apr. 2018.
  20. Dive into Deep Learning. 7.6. Residual Networks (ResNet) [Online]. Available from
  21. Jessin Mathew and Hari S, Traffic Sign Detection using Deep Learning Image Segmentation and CNN, International Research Journal of Engineering and Technology (IRJET), 2021, 8 (5): 989- 993.
If-Else Example