Traffic sign recognition using Convolutional Neural Network
Ayesha Siddiqa K
, Dr. Harish B G , Mr. Chetan Kumar G S , Manvitha M
Image Preprocessing, Feature Extraction, Segmentation, Data Augmentation, Convolution Neural Network convert text to speech.
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.
"Traffic sign recognition using Convolutional Neural Network ", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 9, page no.298 - 301, September-2023, Available :https://ijsdr.org/papers/IJSDR2309048.pdf
Volume 8
Issue 9,
September-2023
Pages : 298 - 301
Paper Reg. ID: IJSDR_208511
Published Paper Id: IJSDR2309048
Downloads: 000347060
Research Area: Master of Computer Application
Country: Shimogga , Karnataka, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
