Comparative Analysis of the Yolo Method with the Ear and Mar Methods for Drowsiness Detection in the Driving Performance Index Evaluation Application
DOI:
https://doi.org/10.33795/jartel.v16i2.9772Keywords:
Computer Vision, Sleepiness Detection, EAR, Flutter, Flask, Mouth Aspect Ratio (MAR), YOLOAbstract
The development of computer vision technology currently allows early detection of a driver's physical condition to improve driving safety. This research aims to develop a mobile-based driver condition monitoring system using the integration of the Flutter framework and the Flask server. This system implements the YOLO (You Only Look Once) algorithm for real-time object detection and Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) analysis to detect symptoms of fatigue or drowsiness through facial images. Image data captured via an Android device is sent to a central server for processing, where the detection results are then stored in a database and displayed back to the user. Test results show that the system is able to classify driver conditions with a high level of accuracy and is able to manage travel sessions in a structured manner. This system is expected to be a preventive solution in reducing the number of traffic accidents caused by human error.
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