Traffic Sign Classification and Detection of Indian Traffic Signs using Deep Learning

Authors

  • Manjiri Bichkar  Department of Computer Engineering, Datta Meghe College of Engineering Airoli, Navi Mumbai, Maharashtra, India
  • Suyasha Bobhate  Department of Computer Engineering, Datta Meghe College of Engineering Airoli, Navi Mumbai, Maharashtra, India
  • Prof. Sonal Chaudhari  Department of Computer Engineering, Datta Meghe College of Engineering Airoli, Navi Mumbai, Maharashtra, India

DOI:

https://doi.org//10.32628/CSEIT217325

Keywords:

CNN, BLOB, EDAS, GTSRB

Abstract

This paper presents an effective solution to detecting traffic signs on road by first classifying the traffic sign images us-ing Convolutional Neural Network (CNN) on the German Traffic Sign Recognition Benchmark (GTSRB)[1] and then detecting the images of Indian Traffic Signs using the Indian Dataset which will be used as testing dataset while building classification model. Therefore this system helps electric cars or self driving cars to recognise the traffic signs efficiently and correctly. The system involves two parts, detection of traffic signs from the environment and classification based on CNN thereby recognising the traffic sign. The classification involves building a CNN model of different filters of dimensions 3 × 3, 5 × 5, 9 × 9, 13 × 13, 15 × 15,19 × 19, 23 × 23, 25 × 25 and 31 ×31 from which the most efficient filter is chosen for further classifying the image detected. The detection involves detecting the traffic sign using YOLO v3-v4 and BLOB detection. Transfer Learning is used for using the trained model for detecting Indian traffic sign images.

References

  1. German Traffic Sign Recognition Benchmark https://www.kaggle.com/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign
  2. Intelligent Transpostation Systems ITS https://en.wikipedia.org/wiki/Intelligent_ transportation_system
  3. Advanced Driver Asssistance System ADAS https://en.wikipedia.org/wiki/Advanced_ driver-assistance_systems
  4. RGB HSI https://www.vocal.com/video/rgb-and-hsvhsihsl-color-space-conversion/
  5. J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel, “The German traffic sign recognition benchmark: a multi-class classification competition,”. in Proc. IEEE IJCNN, 2011, pp. 1453–1460.
  6. Valentyn Sichkar, Sergey A. Kolyubin, ““Effect of various dimension convolutional layer filters on traffic sign classification accuracy”.
  7. Yi Yang, Hengliang Luo, Huarong Xu and Fuchao Wu, Towards Real-Time Traffic Sign Detection and Classification 2014, IEEE.
  8. Kai Li, Weiyao Lan, “Traffic indication symbols recognition with shape context”. Department of Automation Xiamen University, China, 2011, IEEE
  9. DarkNet Framework https://pjreddie.com/darknet

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Published

2021-06-30

Issue

Section

Research Articles

How to Cite

[1]
Manjiri Bichkar, Suyasha Bobhate, Prof. Sonal Chaudhari, " Traffic Sign Classification and Detection of Indian Traffic Signs using Deep Learning, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 3, pp.215-219, May-June-2021. Available at doi : https://doi.org/10.32628/CSEIT217325