Traffic Sign Board Recognition and Voice Alert System using CNN

Yogesh, C.M. and Usha Sree, R. and Hushalictmy, P. (2024) Traffic Sign Board Recognition and Voice Alert System using CNN. INTI JOURNAL, 2024 (08). pp. 1-7. ISSN e2600-7320

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Abstract

Street to guarantee a secure and efficient flow of traffic. Street accidents sometimes occur on account of carelessness in reading traffic signs incorrectly. The suggested framework aids in recognizing the stop sign and giving a voice warning to the motorist for the speaker to make their point, and crucial decisions. The proposed framework is prepared Using a Convolutional Neural Network (CNN), which aids with the recognition and arranging of rush hour congestion sign pictures. To increase precision, a number are of classes generated and characterized on a particular dataset. Utilized was the German Traffic Sign Benchmarks Dataset, which includes 51,900 pictures of road signage in 43 classifications. Around 98.52 percent during execution was precise. After the framework recognizes the sign, the driver is informed through a voice alarm issued through the speaker. The suggested framework also includes a section where drivers are warned about nearby traffic signs so they can keep track of which laws to follow while on a highway. The system’s goal is to protect the driver, passengers, and pedestrians from harm.

Item Type:	Article
Uncontrolled Keywords:	Convolutional Neural Network, Object Detection, Object Classification Traffic, Traffic Signs, Voice Alert
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Depositing User:	Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited:	10 Jul 2024 07:56
Last Modified:	10 Jul 2024 07:56
URI:	http://eprints.intimal.edu.my/id/eprint/1930

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