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INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS - IJCRT (IJCRT.ORG)
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ISSN Approved Journal No: 2320-2882 | Impact factor: 7.97 | ESTD Year: 2013
Call For Paper - Volume 12 | Issue 5 | Month- May 2024
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Published Paper Details:
Paper Title
REAL-TIME ROAD SIGN RECOGNITION SYSTEM USING MACHINE LEARNING AND IMAGE PROCESSING
Authors
Piyush Warule, Satyam Sabale, Kailash Shaw
Keywords
Machine Learning, Digital Image Processing, Computer Vision, Driver Assistance System, PCA, HSV, OpenCV, ANN, Raspberry Pi, OCR.
Abstract
Machine learning and digital image processing are the most frequently used technologies in object detection algorithms. The objective of this work is to formulate a method for traffic light detection and detection of road sign boards. With the help of this proposed work, one can accurately detect and recognize traffic light colors i.e. green, yellow and red, and different signs like speed limit, stop, turn right, turn left, turn back and forward. Road signs are mostly comprised of colors which acts as a basis for distinguishing it from all other objects. Computer vision technique is used in the field of smart and intelligent transport systems. Nowadays, the traffic sign recognition systems have become an integral and functional part of Driver Assistance Systems. Hence this system is proposed to recognize road signs while driving the vehicle and provide audio commands to the drive to assist him accordingly.
IJCRT's Publication Details
Unique Identification Number - IJCRT2101024
Paper ID - 201922
Page Number(s) - 176-180
Pubished in - Volume 9 | Issue 1 | January 2021
DOI (Digital Object Identifier) -
Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Piyush Warule, Satyam Sabale, Kailash Shaw, "REAL-TIME ROAD SIGN RECOGNITION SYSTEM USING MACHINE LEARNING AND IMAGE PROCESSING", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.9, Issue 1, pp.176-180, January 2021, Available at :http://www.ijcrt.org/papers/IJCRT2101024.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
ISSN and 7.97 Impact Factor Details
ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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