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@article{169851,
        author = {Ajay singh and Subrata Sahana},
        title = {Enhancing Road Safety: Real-time Drowsiness Detection System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {2662-2667},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169851},
        abstract = {This paper presents a novel real-time drowsiness detection system that aims to increase road safety by reducing the number of accidents caused by driver fatigue. The system's continuous monitoring of the driver's facial features, eye movements, and head posture is achieved by using an in-vehicle camera machine learning and computer vision. A CNN (Convolutional Neural Network) interprets the video frames and diagnoses various drowsy states such as prolonged eyelid closures and head tilts. Moreover, sensor information — such as steering patterns and braking — as well as vehicle speed — are also utilized to increase the detection accuracy. The system generates drowsy driver auditory and visual alerts instantaneously drowsy drivers to help make immediate actions. High experimental accuracy and responsiveness reveal that the proposed system can efficiently aid in road traffic accidents reduction, thus saving lives.},
        keywords = {Deep Learning; YOLOv8, Attention Detection, Multi-scale Feature Extraction; Swin Transformer},
        month = {November},
        }