Research Article
A Vision-Based Driver Drowsiness Detection Framework for Road Safety Enhancement
@INPROCEEDINGS{10.4108/eai.28-4-2025.2357762, author={Kavya P and Jayaselvi P and Lavanya U and Rajesh N and Prashanth M}, title={A Vision-Based Driver Drowsiness Detection Framework for Road Safety Enhancement}, proceedings={Proceedings of the 4th International Conference on Information Technology, Civil Innovation, Science, and Management, ICITSM 2025, 28-29 April 2025, Tiruchengode, Tamil Nadu, India, Part I}, publisher={EAI}, proceedings_a={ICITSM PART I}, year={2025}, month={10}, keywords={driver monitoring drowsiness detection yolov5 real-time alert system distraction detection road safety}, doi={10.4108/eai.28-4-2025.2357762} }- Kavya P
Jayaselvi P
Lavanya U
Rajesh N
Prashanth M
Year: 2025
A Vision-Based Driver Drowsiness Detection Framework for Road Safety Enhancement
ICITSM PART I
EAI
DOI: 10.4108/eai.28-4-2025.2357762
Abstract
Driver drowsiness and diversion are major causes of street mishaps, posturing noteworthy dangers to street security. It points to create an progressed driver observing framework that leverages YOLOv5 (You Simply See Once) protest location to distinguish early signs of tiredness and diversion. The system uses a custom dataset to detect facial landmarks, such as blinking, turning the head, in order to detect fatigue. It also connects three basic features: automatic call discovery, diversion readiness, yawning detection. The programmable call location aware indicates when the driver is engaged in a call reducing distractions. The diversion sharpness feature sounds an alert when abnormal head movement or changes in the head position are detected, indicating potential fatigue or distraction. The gaping side screens are mouth developments to note extravagance yawning, a valuable indicator of tiredness.
