Development of a Software to Drowsiness Detection for Drivers Using Image Processing and Neural Networks
-
Ali Askari
,
Ali Salehi Sahlabadi
,
Maliheh Eshaghzadeh
,
Mohsen Poursadeghiyan
,
Gebaeil Nasl Saraji
Abstract
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.
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7. Mazloumi E, Moghaddam AS, Abdolshahi A(2021). The Relationship between Subjective Sleepiness and Changes in Breath and Beat Rates among the Clinical Night Workers. Iran J Public Health, 50(10):2149-2151
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12. Nik Afshar N, Kamali M, Aklaghi Pirposhteh E,at al (2023). A Review of the Studies on Driver Drowsiness Detection Sensors and Proposing Hybrid Diagnostic Methods and Efficient Model Design. Journal of Health and Safety at Work, 13 (1) :164-187
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14. Poursadeghiyan M, Mazloumi A, Saraji GN, et al(2017). Determination the levels of subjectiveand observer rating of drowsiness and theirassociations with facial dynamic changes. Iran J Public Health, 46(1):93-102.
15. Othmani A, Sabri AQM, Aslan S, et al (2023). EEG-based neural networks approaches for fatigue and drowsiness detection: A survey. Neurocomputing, 126709.
16. Liu CC, Hosking SG, Lenné MG (2009). Predicting driver drowsiness using vehicle measures: recent insights and future challenges. J Safety Res, 40(4):239-45.
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18. Poursadeghiyan M, Mazloumi A, Nasl Saraji G, et al (2018). Using Image Processing in the Proposed Drowsiness Detection System Design. Iran J Public Health, 47(9):1371-1378.
19. Hirzi MF, Efendi S, Sembiring RW (2021). Literature study of face recognition using the viola-jones algorithm. 2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS), IEEE, pp. 1-6.
20. Karchani M, Mazloumi A, Saraji GN, et al (2015). Association of subjective and interpretive drowsiness with facial dynamic changes in simulator driving. J Res Health Sci, 15 (4):250-5.
21. Askari A, Hosseinpour R, Bakhtiari M, et al (2024) . Investigating the Relationship Between Subjective and Interpretive drowsiness With Lane Departure in Simulator Driving. Health in Emergencies and Disasters Quarterly, 9 (4) :255-264.
22. Verma G, Kumar B (2022). Multi-layer perceptron (MLP) neural network for predicting the modified compaction parameters of coarse-grained and fine-grained soils. Innovative Infrastructure Solutions, 7 (1):78.
23. Lamichhane K, Mazumdar P (2019). Design of Symlet Wavelet based Illumination Normalization Algorithm and its Comparison with other Relevant Algorithms. 2019 42nd International Conference on Telecommunications and Signal Processing (TSP), 580-584.
24. Bergasa LM, Nuevo J, Sotelo MA, et al (2006). Real-time system for monitoring driver vigilance. IEEE Transactions on Intelligent Transportation Systems, 7 (1):63-77.
25. Liu D, Sun P, Xiao Y, et al (2010). Drowsiness detection based on eyelid movement. 2010 second international workshop on education technology and computer science, 49-52.
26. Golz M, Sommer D, Trutschel U, et al (2010). Evaluation of fatigue monitoring technologies. Somnologie, 14 (3):187-199.
27. Klingeberg T, Schilling M (2012). Mobile wearable device for long term monitoring of vital signs. Comput Methods Programs Biomed, 106 (2):89-96.
28. Kobayashi H (2012). EMG/ECG acquisition system with online adjustable parameters using zigbee wireless technology. IEEJ Transactions on Electronics, Information and Systems, 132 (5):632-639.
29. Gomez-Clapers J, Casanella R (2011). A fast and easy-to-use ECG acquisition and heart rate monitoring system using a wireless steering wheel. IEEE Sensors Journal, 12 (3):610-616.
30. Lee B-G, Chung W-Y (2012). Multi-classifier for highly reliable driver drowsiness detection in Android platform. Biomedical Engineering: Applications, Basis and Communications, 24 (02):147-154.
31. George A, Routray A (2016). Real-time eye gaze direction classification using convolutional neural network. 2016 International Conference on Signal Processing and Communications (SPCOM), IEEE, pp. 1-5.
32. Manu B (2016). Facial features monitoring for real time drowsiness detection. 2016 12th International Conference on Innovations in information technology (IIT), IEEE, pp. 1-4.
33. Reddy B, Kim YH, Yun S, et al (2017). Real-time driver drowsiness detection for embedded systems pp. 121–128.using model compression of deep neural networks; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops; Honolulu, HI, USA. 21–26 July 2017;
34. Tipprasert W, Charoenpong T, Chianrabutra C, et al (2019). A method of driver’s eyes closure and yawning detection for drowsiness analysis by infrared camera. In 2019 First international symposium on instrumentation, control, artificial intelligence, and robotics (ICA-SYMP), IEEE, pp. 61-64. January 2019.
35. Lahoti U, Joshi R, Vyas N, et al (2020). Drowsiness detection system for online courses. International Journal of Advanced Trends in Computer Science and Engineering, 9 (2):1930-1934.
2. Sahayadhas A, Sundaraj K, Murugappan M (2012). Detecting driver drowsiness based on sensors: a review. Sensors, 12 (12):16937-53.
3. Poursadeghiyan M, Omidi L, Hami M, et al (2016). Drowsiness and its relation with individual characteristics among night workers in a desert hospital in Iran. Int J Trop Med, 11 (4), 98– 101
4. Poursadeghiyan M, Amjad RN, Baneshi MM, et al (2017). Drowsiness trend in night workers and adaptation to night shift in hospital staff. Ann Trop Med Public Health, 10(4):989–992.
5. Khammar A, Amjad RN, Moghadasi M, et al (2017). Relation between subjective sleepiness and changes in some vital signs among the clinical night workers. Ann Trop med Publ Health, 10 (5): 1179– 1183
6. Khaleghi S, SadeghiMoghaddam A, Abdolshahi A, et al (2020). Association between Blood Pressure and Oral Temperature Rate with Sleepiness Changes among Clinical Night Workers. Iran J Public Health, 49(11):2232–2234.
7. Mazloumi E, Moghaddam AS, Abdolshahi A(2021). The Relationship between Subjective Sleepiness and Changes in Breath and Beat Rates among the Clinical Night Workers. Iran J Public Health, 50(10):2149-2151
8. Karchani M, Kakooei H, Yazdi Z, et al (2011). Do bright-light shock exposures during breaks reduce subjective sleepiness in night workers? Sleep Biol Rhythms, 9:95-102
9. Khammar A, Moghimian M, Ebrahimi MH, et al (2017). Effects of bright light shock on sleepiness and adaptation among night workers of a hospital in Iran. Ann Trop med Public Health, 10 (3): 595– 99
10. Rau P (2005).Drowsy Driver Detection andWarning System for Commercial VehicleDrivers, Field Operational Test Design,Analysis, and Progress. National HighwayTraffic Safety Administration Washington, DC,USA. Paper Number 05-0192
11. Peden AE, Cullen P, Francis KL, et al (2022). Adolescent transport and unintentional injuries: a systematic analysis using the Global Burden of Disease Study 2019. Lancet Public Health, 1;7(8):e657-e669.
12. Nik Afshar N, Kamali M, Aklaghi Pirposhteh E,at al (2023). A Review of the Studies on Driver Drowsiness Detection Sensors and Proposing Hybrid Diagnostic Methods and Efficient Model Design. Journal of Health and Safety at Work, 13 (1) :164-187
13. Zare H, Abdollahi M, Poursadeghiyan M, et al (2022). Epidemiological Study of Fatal Road Accidents in Eastern Iran in a Five-year Period. Health in Emergencies and Disasters Quarterly, 8 (1) :47-54
14. Poursadeghiyan M, Mazloumi A, Saraji GN, et al(2017). Determination the levels of subjectiveand observer rating of drowsiness and theirassociations with facial dynamic changes. Iran J Public Health, 46(1):93-102.
15. Othmani A, Sabri AQM, Aslan S, et al (2023). EEG-based neural networks approaches for fatigue and drowsiness detection: A survey. Neurocomputing, 126709.
16. Liu CC, Hosking SG, Lenné MG (2009). Predicting driver drowsiness using vehicle measures: recent insights and future challenges. J Safety Res, 40(4):239-45.
17. Shahid A, Wilkinson K, Marcu S, et al (2012). STOP, THAT and one hundred other sleep scales. ed. Springer Science & Business Media.
18. Poursadeghiyan M, Mazloumi A, Nasl Saraji G, et al (2018). Using Image Processing in the Proposed Drowsiness Detection System Design. Iran J Public Health, 47(9):1371-1378.
19. Hirzi MF, Efendi S, Sembiring RW (2021). Literature study of face recognition using the viola-jones algorithm. 2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS), IEEE, pp. 1-6.
20. Karchani M, Mazloumi A, Saraji GN, et al (2015). Association of subjective and interpretive drowsiness with facial dynamic changes in simulator driving. J Res Health Sci, 15 (4):250-5.
21. Askari A, Hosseinpour R, Bakhtiari M, et al (2024) . Investigating the Relationship Between Subjective and Interpretive drowsiness With Lane Departure in Simulator Driving. Health in Emergencies and Disasters Quarterly, 9 (4) :255-264.
22. Verma G, Kumar B (2022). Multi-layer perceptron (MLP) neural network for predicting the modified compaction parameters of coarse-grained and fine-grained soils. Innovative Infrastructure Solutions, 7 (1):78.
23. Lamichhane K, Mazumdar P (2019). Design of Symlet Wavelet based Illumination Normalization Algorithm and its Comparison with other Relevant Algorithms. 2019 42nd International Conference on Telecommunications and Signal Processing (TSP), 580-584.
24. Bergasa LM, Nuevo J, Sotelo MA, et al (2006). Real-time system for monitoring driver vigilance. IEEE Transactions on Intelligent Transportation Systems, 7 (1):63-77.
25. Liu D, Sun P, Xiao Y, et al (2010). Drowsiness detection based on eyelid movement. 2010 second international workshop on education technology and computer science, 49-52.
26. Golz M, Sommer D, Trutschel U, et al (2010). Evaluation of fatigue monitoring technologies. Somnologie, 14 (3):187-199.
27. Klingeberg T, Schilling M (2012). Mobile wearable device for long term monitoring of vital signs. Comput Methods Programs Biomed, 106 (2):89-96.
28. Kobayashi H (2012). EMG/ECG acquisition system with online adjustable parameters using zigbee wireless technology. IEEJ Transactions on Electronics, Information and Systems, 132 (5):632-639.
29. Gomez-Clapers J, Casanella R (2011). A fast and easy-to-use ECG acquisition and heart rate monitoring system using a wireless steering wheel. IEEE Sensors Journal, 12 (3):610-616.
30. Lee B-G, Chung W-Y (2012). Multi-classifier for highly reliable driver drowsiness detection in Android platform. Biomedical Engineering: Applications, Basis and Communications, 24 (02):147-154.
31. George A, Routray A (2016). Real-time eye gaze direction classification using convolutional neural network. 2016 International Conference on Signal Processing and Communications (SPCOM), IEEE, pp. 1-5.
32. Manu B (2016). Facial features monitoring for real time drowsiness detection. 2016 12th International Conference on Innovations in information technology (IIT), IEEE, pp. 1-4.
33. Reddy B, Kim YH, Yun S, et al (2017). Real-time driver drowsiness detection for embedded systems pp. 121–128.using model compression of deep neural networks; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops; Honolulu, HI, USA. 21–26 July 2017;
34. Tipprasert W, Charoenpong T, Chianrabutra C, et al (2019). A method of driver’s eyes closure and yawning detection for drowsiness analysis by infrared camera. In 2019 First international symposium on instrumentation, control, artificial intelligence, and robotics (ICA-SYMP), IEEE, pp. 61-64. January 2019.
35. Lahoti U, Joshi R, Vyas N, et al (2020). Drowsiness detection system for online courses. International Journal of Advanced Trends in Computer Science and Engineering, 9 (2):1930-1934.
| Files | ||
| Issue | Vol 54 No 9 (2025) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/ijph.v54i9.19869 | |
| Keywords | ||
| Driver monitoring system Software drowsiness detection Neural network Viola-Jones algorithm Image processing | ||
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Askari A, Salehi Sahlabadi A, Eshaghzadeh M, Poursadeghiyan M, Nasl Saraji G. Development of a Software to Drowsiness Detection for Drivers Using Image Processing and Neural Networks. Iran J Public Health. 2025;54(9):2024-2034.
