Tehran University of Medical Sciences Iranian Journal of Public Health 2251-6085 54 9 2025 10 04 2024 2034 EN Ali Askari 1. Department of Occupational Health Engineering, School of public Health, Tehran University of Medical Sciences, Tehran, Iran 2. Department of Occupational Health and Safety, OICO, Azar Oilfield Project, Ilam, Iran Ali Salehi Sahlabadi 1. Safety Promotion and Injury Prevention Research Center, Research Institute for Health Sciences and Environment, Shahid Beheshti University of Medical Sciences, Tehran, Iran 2. Department of Occupational Health and Safety, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran Maliheh Eshaghzadeh Department of Nursing, school of Nursing and Midwifery, Torbat Heydariyeh University of Medical Sciences, Torbat Heydariyeh, Iran Mohsen Poursadeghiyan 1. Social Determinants of Health Research Center, School of Health, Ardabil University of Medical Sciences, Ardabil, Iran 2. Department of Occupational Health Engineering, School of Health, Ardabil University of Medical Sciences, Ardabil, Iran6. Social Determinants of Health Research Center, School of Health, Ardabil University of Medical Sciences, Ardabil, Iran 7. Department of Occupational Health Engineering, School of Health, Ardabil University of Medical Sciences, Ardabil, Iran Gebaeil Nasl Saraji Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran 2024 07 11 2025 07 17 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. https://ijph.tums.ac.ir/index.php/ijph/article/view/35500 https://ijph.tums.ac.ir/index.php/ijph/article/download/35500/8658