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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM35203

Traffic Sign Detection Under Foggy Environment Using Machine Learning

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM35203

Volume:

8 |

5 |

May 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM35203

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

MS

Mr.Vishal Sonar

MD

Mrs.Manisha Darak

MT

Mr.Sujit Tatugade

MT

Mr.Mandar Todkar

MM

Ms.Vaishnavi Mugale

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Abstract

Road signs are important to ensure smooth traffic flow without bottle necks or mishaps. Road symbols are the pictorial representations having different necessary information required to be understood by driver. Road signs in front of the vehicle are ignored by the drivers and this can lead to catastrophic accidents. This paper presents an overview of the traffic sign board detection and recognition and implements a procedure to extract the road sign from a natural complex image, processes it and alerts the driver using voice command It is implemented in such a way that it acts as a boon to drivers to make easy decisions. Traffic sign recognition is a critical component of modern intelligent transportation systems, contributing to road safety and efficient traffic management. This paper presents a novel approach to traffic sign recognition based on Convolutional Neural Networks (CNNs).

KEYWORDS

CNN Pre-processing Feature Extraction.

Cite This Article

Mr.Vishal Sonar, Mrs.Manisha Darak, Mr.Sujit Tatugade, Mr.Mandar Todkar, Ms.Vaishnavi Mugale, "Traffic Sign Detection Under Foggy Environment Using Machine Learning," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 5, May 2024. https://doi.org/10.55041/IJSREM35203

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Publication Date

12/31/45442

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Double-Blind Peer Review

Review Rounds: 2

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5

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The authors declare no conflict of interest related to this research work.

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