Two wheelers prove to be an easy and comfortable way of transportation, however, it also comes with its own set of risks. The Ministry of Road Transport and Highways recorded close to 50,000 deaths in the year 2020 alone. A major part of these accidents stem from the fact that the drivers of these vehicles do not have visual access behind their vehicle or limited access therefore resulting in less adequate reactions. While various works have been done for a collision warning system, they majorly concentrate on four wheelers. In particular, deep learning methodologies have been widely used in developing these systems. This paper proposes one such framework that leverages the advantages of YoloR (a deep learning-based object detection algorithm) to develop a smart helmet that is installed with additional voice assistant features to ease the experience for two-wheeler drivers. A prototype basic helmet design of helmet is provided that will be useful for further researchers and designers to develop more effective and better such systems in future.


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    Titel :

    Computer Vision-Based Smart Helmet with Voice Assistant for Increasing Driver Safety


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Unhelkar, Bhuvan (Herausgeber:in) / Pandey, Hari Mohan (Herausgeber:in) / Agrawal, Arun Prakash (Herausgeber:in) / Choudhary, Ankur (Herausgeber:in) / Grover, Yash (Autor:in) / Mishra, Shreedev Ganesh (Autor:in) / Kumar, Susheet (Autor:in) / Verma, Priyank (Autor:in) / Nayak, Soumya Ranjan (Autor:in)

    Kongress:

    International Conference on Advances and Applications of Artificial Intelligence and Machine Learning ; 2022 ; Noida, India September 16, 2022 - September 17, 2022



    Erscheinungsdatum :

    15.11.2023


    Format / Umfang :

    14 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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