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ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15

Issue: September 2023

Volume 8 | Issue 9

Impact factor: 8.15

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Paper Title: Traffic sign recognition using Convolutional Neural Network
Authors Name: Ayesha Siddiqa K , Dr. Harish B G , Mr. Chetan Kumar G S , Manvitha M
Unique Id: IJSDR2309048
Published In: Volume 8 Issue 9, September-2023
Abstract: The traffic signs on the road express a number of cautions. By assisting tourists achieving their final locations and informing them in ahead of time, leave, and turn locations, they maintain traffic flow. To guarantee the safety of drivers, road signs are set in certain locations. Additionally, they offer instructions on when and location cars should either turn or not. In this research, we developed a technique for taking a road sign out of a naturally complicated image, processing it, warning the motorist by voice order. We also proposed a technique for locating and recognizing traffic indicators. It is used in a way that makes quick decisions possible for driver. Traffic sign detection is difficult in actual time due to variables such changing weather, shifting light directions, and varied light intensity. The incomplete noise or complete substantial changes in color saturation, partial or complete underexposure, or full-on overexposure, a wide range of viewing angles, view depth, and traffic sign shape and color distortions (caused by light intensity) are just a few of the factors that can affect a machine's reliability. Three phases make up the suggested architecture. The first step is image pre-processing, where we decide on the learning input size, adjust the data for the learning stage, and size the input files for the dataset. In the course of the recognition process, the suggested algorithm sorts the observed symbol. This is accomplished in the second phase using a Convolutional Neural Network, and in the third step, text speech displacement is dealt with, the recognized evidence of the second phase delivered in audio file.
Keywords: Image Preprocessing, Feature Extraction, Segmentation, Data Augmentation, Convolution Neural Network convert text to speech.
Cite Article: "Traffic sign recognition using Convolutional Neural Network ", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 9, page no.298 - 301, September-2023, Available :http://www.ijsdr.org/papers/IJSDR2309048.pdf
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Publication Details: Published Paper ID: IJSDR2309048
Registration ID:208511
Published In: Volume 8 Issue 9, September-2023
DOI (Digital Object Identifier):
Page No: 298 - 301
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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