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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM34852

TRAFFIC SIGN RECOGNITION AND DETECTION using CNN

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM34852

Volume:

8 |

5 |

May 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/ijsrem34852

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

MNH

Mrs. Nivedita Hebbale

STS

Sanchitha T S

SHT

Sushmitha H T

TTJ

Thejashwini T J

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Abstract

Roadside traffic indicators including yield and speed restriction signs are automatically detected using traffic sign classification. The evolution of traffic sign recognition systems that operate automatically regarding “smarter automobiles.” For self-driving cars to effectively understand and comprehend the road, the need for  recognizing traffic signs. Similar to this, for the purpose of support and safeguard drivers, “driver alert” systems in automobiles need to comprehend the surrounding road. With our technology, drivers wouldn’t have to take their eyes off the road to see and understand traffic signs. Convolution neural networks allow for accurate classification of the signboards. More layers can be added to increase the precision. Here, training and testing are done using the GTSRB dataset; by optimizing the settings, traffic signs are reliably classified into 43 types, and detection speed also rises.

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Cite This Article

Mrs. Nivedita Hebbale, Sanchitha T S, Sushmitha H T, Thejashwini T J, "TRAFFIC SIGN RECOGNITION AND DETECTION using CNN," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 5, May 2024. https://doi.org/10.55041/ijsrem34852

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Publication Date

5/27/2024

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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

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