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SMART DROWSINESS DETECTION: ENHANCING DRIVER SAFETY WITH OPENCV AND DEEP LEARNING

AUTHORS:
Lammata Shiva Prasad Babu , Mohammad Moulana
Mentor
Affiliation
Department of CSE, Koneru Lakshmaiah Education Foundation
DOI: 10.55041/ijsmt.v2i2.052
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract
Eye detection as to whether the eyes are open or closed is significant for many applications, such as driver fatigue detection, authentication systems, and medical diagnostics. This paper discusses methodologies of real-time and offline eye detection using ML and DL models. The paper further elaborates on key feature extraction techniques, CNN architectures, and optimization strategies to improve accuracy.The study culminates into a model that shows 98.7% accuracy with robustness against varying illumination and occlusions.
Keywords
Eye Detection Facial Landmarks Convolutional Neural Networks Computer Vision Fatigue Detection Deep Learning.
Article Information
PUBLICATION DETAILS
Review Type:
0
License:
CC BY-NC 4.0
Volume/Issue:
Volume Volume 10, Issue 01
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Feb 19 2026
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Moulana, L. S. P. B. ,. M. (2026). Smart Drowsiness Detection: Enhancing Driver Safety with Opencv and Deep Learning. International Journal of Science, Strategic Management and Technology, Volume 10(01). https://doi.org/10.55041/ijsmt.v2i2.052

Moulana, Lammata. "Smart Drowsiness Detection: Enhancing Driver Safety with Opencv and Deep Learning." International Journal of Science, Strategic Management and Technology, vol. Volume 10, no. 01, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i2.052.

Moulana, Lammata. "Smart Drowsiness Detection: Enhancing Driver Safety with Opencv and Deep Learning." International Journal of Science, Strategic Management and Technology Volume 10, no. 01 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i2.052.

References

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  3. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

  4. Krizhevsky, , Sutskever, I., & Hinton, G.

  5. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems (NIPS), 25.

  6. Howard, G., et al. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.

  7. Dalal, , & Triggs, B. (2005). Histograms of oriented gradients for human detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR).

  8. Soukupová, T., & Čech, J. (2016). Real- time eye blink detection using facial landmarks. Proceedings of the 21st Computer Vision Winter Workshop (CVWW).

  9. Tieleman, , & Hinton, G. (2012). Lecture 6.5—RMSProp: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning.

  10. Redmon, J., & Farhadi, A. (2018). YOLOv3: An incremental arXiv preprint arXiv:1804.02767.

  11. Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Ethics and Compliance
✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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