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Monitoring System of Drowsiness and Lost Focused Driver Using Raspberry Pi.
Kusworo Adi1, Catur Edi Widodo1, Aris Puji Widodo2
1Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Semarang, Indonesia.
Iranian Journal of Public Health
|March 1, 2021
Summary
This study developed a real-time driver monitoring system using image processing to detect drowsiness and loss of focus. The system achieved high accuracy, offering a promising approach to prevent accidents caused by fatigued or distracted driving.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Automotive Safety
Background:
- Driver drowsiness and loss of focus are significant contributors to road accidents.
- Developing effective methods to detect these conditions in real-time is crucial for accident prevention.
Purpose of the Study:
- To develop and evaluate an image processing-based system for real-time detection of driver drowsiness and loss of focus.
- To enhance road safety by providing an early warning system for at-risk drivers.
Main Methods:
- Utilized Raspberry Pi for real-time video processing.
- Employed the Haar Cascade Classifier technique to identify facial features (eyes, mouth) indicative of drowsiness.
- Analyzed detected features to determine driver's state of focus and drowsiness.
Main Results:
- The system successfully identified two key parameters: driver loss of focus and drowsiness.
- Achieved a highest accuracy of 88.00% for detecting a lost-focused driver.
- Reached a highest accuracy of 90.40% for detecting a drowsy driver.
Conclusions:
- The developed image processing system effectively monitors driver drowsiness and loss of focus with high accuracy.
- The system demonstrates potential for real-world application in enhancing road safety.
- Further improvements are recommended to optimize system performance.
