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@article{156032,
        author = {A.S.Muthanandha Murugavel and Hemakarthika.M and Reena Rethiga.R and Tharini.R},
        title = {Distracted Driver Detection and Alert System using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {2},
        pages = {751-755},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=156032},
        abstract = {Driver distractedness and interruption are the primary drivers of street mishaps, a significant number of which bring about fatalities. At present, interruption location frameworks for street vehicles are not yet broadly accessible or are restricted to explicit reasons for driver distractedness, for example, driver weariness. Research endeavors have been made to screen drivers' attentional states and offer help to drivers. The current work of occupied driver recognition is worried about a restricted arrangement of interruptions (Mainly phone utilization). In this venture, a strong driver interruption discovery framework that extricates the driver's state from the accounts of a Windshield camera utilizing Deep Learning based Faster Region Convolutional Neural Network (FRCNN). This venture utilizes the state ranch diverted driver identification, which contains four classes: calling, messaging, looking behind, and ordinary driving. The fundamental component of the proposed arrangement is the extraction of the driver's body parts, utilizing profound learning-based division, prior to playing out the interruption discovery and grouping task. The typical exactness of the proposed arrangement surpasses 96% on our dataset. The class actuation map (CAM) of our proposed strategy is abstractly more sensible, which would upgrade the unwavering quality and make sense of capacity of the model.},
        keywords = {},
        month = {},
        }