A Study on Neural Networks used for Traffic Signal Classification

References

[1] Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel. The german traffic sign recognition benchmark: a multi-class classification competition. In Neural Networks (IJCNN), The 2011 International Joint Conference on, pages 1453–1460. IEEE, 2011.

[2] Andreas Møgelmose, Mohan M. Trivedi, and Thomas B. Moeslund, Vision based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey,” IEEE Transactions on Intelligent Transportation Systems, 2012.

[3] Yanjun Fan and Weigong Zhang, Traffic sign detection and classification for Advanced Driver Assistant Systems, 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2015, pp. 1335-1339, doi: 10.1109/FSKD.2015.7382137.

[4] A. Ruta, Y. Li, and X. Liu, Robust class similarity measure for traffic sign recognition, IEEE Trans. Intell. Transp. Syst., vol. 11, no. 4, pp. 846–855, Dec. 2010.

[5] Haloi, Mrinal, 2015. Traffic sign classification using deep inception based convolutional networks. CoRR, abs/1511.02992

[6] D. Tabernik and D. Skocaj,ˇDeep Learning for Large-Scale TrafficSign Detection and Recognition, in IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 4, pp. 1427-1440, April 2020, doi: 10.1109/TITS.2019.2913588.

[7] A. Nandewal,A. Tripathi and S. Chandrra , Indian Traffic Sign Detection and Classification Using Neural Networks,2nd International Congress of Technology, Management and Social Sciences-16 (ICTMS-16)

[8] A. Krizhevsky, I. Sutskever, and G. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proc. Adv. Neural Inf. Process. Syst., 2012, vol. 25, pp. 1106–1114.

[9] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proc. IEEE, vol. 86, no. 11, pp. 2278– 2324, Nov. 1998

[10] J. Jin, K. Fu and C. Zhang, Traffic Sign Recognition With Hinge Loss Trained Convolutional Neural Networks, in IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 5, pp. 1991-2000, Oct. 2014, doi: 10.1109/TITS.2014.2308281.

[11] Timofte, Radu, Karel Zimmermann, and Luc Van Gool. Multi-view traffic sign detection, recognition, and 3D localisation. Machine vision and applications 25.3 (2014): 633-647.

[12] Chaudhari, Tejas and Wale, Ashish and Joshi, Amit and Sawant, Suraj, Traffic Sign Recognition Using Small-Scale Convolutional Neural Network, (May 3, 2020). 2nd International Conference on Communication and Information Processing (ICCIP) 2020

[13] Kingma, Diederik & Ba, Jimmy.Adam: A Method for Stochastic Optimization. International Conference on Learning Representations, (2014)

[14] Agarap, Abien Fred. (2018). Deep Learning using Rectified Linear Units (ReLU).

[15] J. Bouvrie, Notes on convolutional neural networks, 2006

[16] D. Scherer, A. Muller, and S. Behnke,¨“Evaluation of pooling operations in convolutional architectures for object recognition,” in Artificial Neural Networks—ICANN. Berlin, Germany: Springer-Verlag, 2010, pp. 92–101

[17] Kumar, Amara Dinesh.“Novel Deep Learning Model for Traffic Sign Detection Using Capsule Networks.” ArXiv abs/1805.04424 (2018): n. pag.

@article{1703982,
      author = {Aditya Kulkarni, Omkar Hase, Hetavi Gandhi, Asst. Prof. Kopal Gangrade, Asst. Prof. Shweta Shah},
      title = {A Study on Neural Networks used for Traffic Signal Classification},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {7},
      pages = {24-29},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703982.pdf},
      abstract = {Traffic Recognition System (TRS) plays a key role in saving millions of lives every year.It recognizes the signs placed beside the road. Many a times such signs are partially covered leading to confusion. The TRS enables the drivers to identify these signs beforehand which in turn decreases the chances of an accident occurring. Technology behind the TRS has evolved over the years and Neural Networks (CNN) has been on the forefront of this evolution. CNN models are specifically used for object detection and are widely used. In this article we would analyse these CNN models along with a brief implementation of the same. The dataset used for this are highly regarded and have been the basis of several research papers.},
      keywords = {Neural Networks, Computer Vision, CNN, Image Processing},
      month = {January},
  }