Traffic Sign Board Recognition and Voice Alert System using CNN
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English English |
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INTI International University
2024
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/1930/ http://eprints.intimal.edu.my/1930/2/473 http://eprints.intimal.edu.my/1930/3/ij2024_08r.pdf |
| _version_ | 1848766969977765888 |
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| author | Yogesh, C.M. Usha Sree, R. Hushalictmy, P. |
| author_facet | Yogesh, C.M. Usha Sree, R. Hushalictmy, P. |
| author_sort | Yogesh, C.M. |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:58:05Z |
| format | Article |
| id | intimal-1930 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:59:36Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-19302025-07-30T06:48:48Z http://eprints.intimal.edu.my/1930/ Traffic Sign Board Recognition and Voice Alert System using CNN Yogesh, C.M. Usha Sree, R. Hushalictmy, P. Q Science (General) QA75 Electronic computers. Computer science QA76 Computer software 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. INTI International University 2024-07 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/1930/2/473 text en cc_by_4 http://eprints.intimal.edu.my/1930/3/ij2024_08r.pdf 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 https://intijournal.intimal.edu.my |
| spellingShingle | Q Science (General) QA75 Electronic computers. Computer science QA76 Computer software Yogesh, C.M. Usha Sree, R. Hushalictmy, P. Traffic Sign Board Recognition and Voice Alert System using CNN |
| title | Traffic Sign Board Recognition and Voice Alert System using CNN |
| title_full | Traffic Sign Board Recognition and Voice Alert System using CNN |
| title_fullStr | Traffic Sign Board Recognition and Voice Alert System using CNN |
| title_full_unstemmed | Traffic Sign Board Recognition and Voice Alert System using CNN |
| title_short | Traffic Sign Board Recognition and Voice Alert System using CNN |
| title_sort | traffic sign board recognition and voice alert system using cnn |
| topic | Q Science (General) QA75 Electronic computers. Computer science QA76 Computer software |
| url | http://eprints.intimal.edu.my/1930/ http://eprints.intimal.edu.my/1930/ http://eprints.intimal.edu.my/1930/2/473 http://eprints.intimal.edu.my/1930/3/ij2024_08r.pdf |