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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM3396
Volume:
4 |
4 |
Apr 2020
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
The Driver Monitoring System (DMS) is an innovative solution aimed at reducing road accidents caused by driver fatigue, distraction, and other unsafe behaviors. Leveraging advanced technologies like Convolutional Neural Networks (CNNs) and YOLO object detection models, the system monitors critical parameters such as eye movements, blinking frequency, and facial expressions. This real-time analysis enables the detection of drowsiness, distractions, and risky behaviors, issuing timely alerts to ensure driver safety. CNNs, with their superior feature extraction and classification capabilities, outperform traditional methods by offering high accuracy, robustness to variations, and scalability. This project demonstrates the efficacy of integrating CNNs into DMS, paving the way for safer driving environments and significant advancements in automotive safety.
KEYWORDS
Publication Date
12/4/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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