Development of a Software to Drowsiness Detection for Drivers Using Image Processing and Neural Networks
Ali Askari1,2, Ali Salehi Sahlabadi3,4, Maliheh Eshaghzadeh5
1Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Background:
During driving, drowsiness may happen for a few moments, but its consequences can be terrible. Drowsiness in the driver can be detected in the early stages. Each method used for detecting drowsiness has its own strengths and weaknesses or benefits and flaws. The main contribution of our research was improving Driver Drowsiness Detection (D.D.D) systems.
Methods:
In accordance with the research objective, it is imperative to address the subsequent inquiries (Q) throughout the process of constructing, testing, and delivering the ultimate D.D.D software model: Q1. What is the methodology employed for constructing the initial model of drowsiness detection software? Q2. How is the initial model of drowsiness detection software tested and refined during the development phase? Q3. What is the operational mechanism of the final model of drowsiness detection software?
Results:
The results were able to detect different facial conditions (with hair and glasses) with a 92.3 percentage detection rate.
Conclusion:
This model could help improve D.D.D systems, and detect drowsiness in different environments and situations.
