In a world where millions of people are wounded in auto accidents each year due to negligence, a lack of understanding of traffic laws, and bad weather, there is an urgent need for greater road safety. This is particularly the case in India, where a disproportionately high number of traffic accidents lead to numerous fatalities. Ignoring traffic signs raises these risks and endangers not only vehicles but also passengers and pedestrians. This project addresses the significant issue of traffic sign recognition in bad weather and offers voice-based instruction in many languages to increase road safety. Using a mix of state-of-the-art technologies, including YOLOv8 for real-time sign detection and the Google Translate API, which supports NLP tasks, this research offers a full solution. The model's remarkable precision and efficacy underscore its capacity to revolutionize traffic safety and furnish a more secure and expedient driving encounter. With the world moving towards more autonomous mobility, this study is laying the groundwork for safer and more effective driving in the future.
Multilingual Voice-Assisted for Traffic Sign Detection and Classification in Adverse Weather Conditions
Lect. Notes in Networks, Syst.
International Conference on Recent Trends in Machine Learning, IOT, Smart Cities & Applications ; 2024 ; Hyderabad, India March 28, 2024 - March 29, 2024
Proceedings of 5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications ; Kapitel : 36 ; 425-436
28.02.2025
12 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Inclement weather , Multiple languages , Traffic signs , Google translation API , Road safety , Traffic laws , Voice-based guidance , YOLOv8 Systems and Data Security , Cyber-physical systems, IoT , Artificial Intelligence , Engineering , Machine Learning , Wireless and Mobile Communication , Professional Computing
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