A Review of IoT-Based Driver Drowsiness Detection Systems
Anishka Sharma
, Garv Verma , Manas Mittal , Manmay Garg , Meetu Rani
This paper presents a comprehensive review of driver drowsiness detection systems with a specific focus on Internet of Things (IoT) implementations using Raspberry Pi. Facial landmark detection, Eye Aspect Ratio (EAR), and Mouth Aspect Ratio (MAR) integration with IoT platforms are among the detection methods examined in this study. The computer vision-based approaches that make use of the OpenCV and Dlib libraries are the focus of the main benefits, drawbacks, and potential uses. The review highlights the effectiveness of non-intrusive, camera-based methods that achieve detection accuracies of 75-90% depending on environmental conditions. Gaps in current research are identified, particularly concerning performance in low-light conditions and with facial occlusions. Future research directions are suggested to enhance detection accuracy and system reliability through infrared imaging, deep learning models, and multi-sensor fusion techniques integrated with IoT frameworks for enhanced road safety applications.
"A Review of IoT-Based Driver Drowsiness Detection Systems", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.10, Issue 3, page no.c478-c483, March-2025, Available :https://ijsdr.org/papers/IJSDR2503259.pdf
Volume 10
Issue 3,
March-2025
Pages : c478-c483
Paper Reg. ID: IJSDR_301203
Published Paper Id: IJSDR2503259
Downloads: 000172
Research Area: Science and Technology
Country: meerut, uttar pradesh, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
