Traffic Signs Recognization using Machine Learning

Vaka Neelima; Cherukuri Nayomi; Arla Prasanna Kumari; Munnangi Ravi Teja; Mr. K Sivakrishna; Dr. M. Sreenivasulu

doi:10.55524/ijirem.2022.9.2.105

Abstract

The expansive road network in India is responsible for the movement of the vast majority of the country's products as well as its population. Intelligent transit systems are one example of the cutting-edge technology that has been developed and implemented over the course of the past three decades to enhance the safety of public transportation and reduce emissions. Other examples of this cutting-edge technology include autonomous vehicles and magnetic levitation. (ITS). In spite of the difficulties, there is still a sizeable scholarly community that is interested in researching methods that are predicated on ITS for the purpose of identifying traffic signals. These researchers are trying to figure out how to better collect and analyze impulses, specifically at night or in conditions where there is restricted illumination. Specifically, they are focusing on the nighttime circumstances. The course of this research led to the development of a number of strategies for accelerating the procedures of form model extraction, segmentation, and feature extraction. These strategies were presented throughout the course of the study. When a person has more experience, they should be able to realistically anticipate a higher general rate of accurate identifications.

Keywords

Traffic, Signs, Recognization, Machine, Learning.

Reference

Andrej, K, Janez, B &Andrej, K 2011, ‘Introduction to the Artificial Neural Networks. Artificial Neural Networks-Methodological Advances and Biomedical Application, pp. 1-18.
Brkic, K 2010, ‘An overview of traffic sign detection methods’, Department of Electronics, Microelectronics, Computer and Intelligent Systems Faculty of Electrical Engineering and Computing Unska, pp. 1-9.
Changzhen, X, Cong, W, Weixin, M &Yanmei, S 2016, ‘A traffic sign detection algorithm based on deep convolutional neural network’, IEEE International Conference Signal and Image Processing (ICSIP), pp. 676-679.
De la Escalera, A, Armingol, JM & Mata, M 2003, ‘Traffic sign recognition and analysis for intelligent vehicles’, Image and vision computing, vol. 21, no. 3, pp. 247-258.
Dhar, P, Abedin, MZ, Biswas, T &Datta, A 2017, ‘Traffic sign detection—A new approach and recognition using convolution neural network’, IEEE Region Humanitarian Technology Conference (R10- HTC), pp. 416-419.
Fang, C, Chen, S, &Fuh, C 2003, ‘Road-sign detection and tracking,’ IEEE Trans on Vehicular Technology, vol. 52, pp. 1329-1341.
Han, J, Kamber, M 2006, ‘Data Mining: Concepts and Techniques’, Second Edition Morgan Kaufmann, pp. 1-740.
Khan, JF, Adhami, RR &Bhuiyan, SM 2009, ‘Image segmentationbased road sign detection’, IEEE Southeastcon, pp. 24-29.
PrasaduPeddi (2018), “A Study For Big Data Using Disseminated Fuzzy Decision Trees”, ISSN: 2366- 1313, Vol 3, issue 2, pp:46-57.
Li, Y & Wang, W 2011, ‘Recognition of traffic signs by artificial neural network’, International Journal of Digital Library Systems, vol. 2, no. 4, pp. 1–12.
Shneier, M 2006, ‘Road sign detection and recognition’, Unmanned Systems Technology International Society for Optics and Photonics, pp. 1-6.

Cites this article as

V. Neelima, C. Nayomi, A. P. Kumari, M. R. Teja, M. K. Sivakrishna, D. M. Sreenivasulu, "Traffic Signs Recognization using Machine Learning", International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-9, Issue-2, Page No-656- 660, 2022. Available from: https://doi.org/10.55524/ijirem.2022.9.2.105