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@article{176446,
        author = {Disha Y and Achinthya Upadhyaya},
        title = {A YOLO and Machine Learning-Based Framework for Real-Time Driver Drowsiness Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {5262-5267},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176446},
        abstract = {Drowsiness is a major factor in accidents and failures across transportation, industry, and surveillance settings. This research proposes a real-time, non-intrusive fatigue detection system using computer vision and machine learning, centered on the YOLO object detection model. Unlike intrusive physiological methods like EEG, the system relies on visual cues—such as eye aspect ratio, yawning, and head movement—for early drowsiness detection. YOLOv8 is used for fast localization of facial regions, followed by machine learning-based classification into drowsy or alert states. The pipeline is modular and designed for real-time operation, currently validated under controlled indoor conditions. Though broader deployment and adaptive enhancements are future goals, the system demonstrates a practical and scalable baseline for intelligent fatigue monitoring.},
        keywords = {Drowsiness detection, Eye aspect ratio (EAR), Real-time monitoring, YOLOv8},
        month = {April},
        }