Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{155770,
        author = {Priyanka Andhavarapu and Gudla Likitha and Gujju Priya Devi and Sampathirao Sai Kiran and Dr.CH.Ramesh},
        title = {Drowsiness detection system},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {1},
        pages = {1732-1735},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=155770},
        abstract = {Drowsiness detection is a safety technology that can help drivers avoid accidents caused by falling asleep while driving. One of the potential applications of intelligent car systems is drowsy driver detection. Drowsiness detection mostly relies on assumptions about blink rate and ocular closure. We use machine learning to identify genuine human behaviour during sleepiness episodes in our study. We'll use OpenCV in this Python project to collect photos from a camera and feed them into a Deep Learning model that will classify whether the person's eyes are 'Open' or 'Closed.' A convolutional neural network is a sort of deep neural network that excels at picture classification},
        keywords = {Open CV ,Haar cascade classifiers, Blink rate.},
        month = {},
        }