Preventing Drowsy Driving Accidents in the Construction Industry Using Computer Vision and Convolutional Neural Networks
Publication: Construction Research Congress 2024
ABSTRACT
Construction machine operators are at risk of drowsy driving, which can lead to accidents and injuries on jobsites. Jobsite injuries are a major concern in the construction industry, with operators of heavy machinery being particularly vulnerable. According to the US Bureau of Labor Statistics, in 2019, the fatal injury rate for workers in the construction industry was higher than the national average for all workers, and many of these fatal injuries were caused by heavy equipment. To address this issue, a method using computer vision and edge devices has been proposed to detect drowsy construction machine drivers using convolutional neural networks (CNNs). A dataset of images showing signs of drowsiness was constructed and used to train the CNN. After training, the neural network can score the testing images and predict if the subject in the image is drowsy or non-drowsy. This technology has the potential to greatly improve safety in the construction industry by alerting drowsy operators before an accident occurs, thus reducing the risk of accidents and injuries on jobsites. The implementation of this technology can help in reducing jobsite injuries and improve the overall safety of construction sites.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Adão Martins, N. R., S. Annaheim, C. M. Spengler, and R. M. Rossi. 2021. 'Fatigue monitoring through wearables: A state-of-the-art review', Frontiers in physiology, 12: 2285.
Ahmad, I., and K. Pothuganti. 2020. “Design & implementation of real time autonomous car by using image processing & IoT.” In 2020 Third international conference on smart systems and inventive technology (ICSSIT), 107–13. IEEE.
BLS (US Bureau of Labor Statistics). 2022. National Census of Fatal Occupational Injuries 2021.
Cascudo, I., B. David, L. Garms, and A. Konring. 2023. “YOLO YOSO: fast and simple encryption and secret sharing in the YOSO model.” In Advances in Cryptology–ASIACRYPT 2022: 28th International Conference on the Theory and Application of Cryptology and Information Security, Taipei, Taiwan, December 5–9, 2022, Proceedings, Part I, 651-80. Springer.
Chen, J., H. Li, L. Han, J. Wu, A. Azam, and Z. Zhang. 2022. 'Driver vigilance detection for high-speed rail using fusion of multiple physiological signals and deep learning', Applied Soft Computing, 123: 108982.
Ge, L., D. Dan, and H. Li. 2020. 'An accurate and robust monitoring method of full‐bridge traffic load distribution based on YOLO‐v3 machine vision', Structural Control and Health Monitoring, 27: e2636.
Hawkins, D., and A. Luana Chavarria. 2023. 'Fatal Injuries in the Health Care and Social Assistance Industry, Census of Fatal Occupational Injuries, 2011 to 2019', Journal of Occupational and Environmental Medicine, 65: 167–71.
Iwamoto, H., K. Hori, K. Fujiwara, and M. Kano. 2021. 'Real-driving-implementable drowsy driving detection method using heart rate variability based on long short-term memory and autoencoder', IFAC-PapersOnLine, 54: 526–31.
Jayasree, V., and M. Nivetha Kumari. 2020. “IOT based smart helmet for construction workers.” In 2020 7th International Conference on Smart Structures and Systems (ICSSS), 1–5. IEEE.
Ji, Y., S. Wang, Y. Zhao, J. Wei, and Y. Lu. 2019. 'Fatigue state detection based on multi-index fusion and state recognition network', IEEE Access, 7: 64136–47.
Kapoor, N. S., M. Anand, S. Tiwari, S. Shivani, and R. Singh. 2023. 'Real Time Face Detection-based Automobile Safety System using Computer Vision and Supervised Machine Learning.' in, Advancement of Deep Learning and its Applications in Object Detection and Recognition (River Publishers).
Kazan, E., and M. A. Usmen. 2018. 'Worker safety and injury severity analysis of earthmoving equipment accidents', Journal of safety research, 65: 73–81.
Léger, D., E. Pepin, and G. Caetano. 2019. 'The economic burden of sleepy driving', Sleep medicine clinics, 14: 423–29.
Li, J., H. Li, W. Umer, H. Wang, X. Xing, S. Zhao, and J. Hou. 2020. 'Identification and classification of construction equipment operators’ mental fatigue using wearable eye-tracking technology', Automation in Construction, 109: 103000.
Li, J., H. Li, H. Wang, W. Umer, H. Fu, and X. Xing. 2019. 'Evaluating the impact of mental fatigue on construction equipment operators’ ability to detect hazards using wearable eye-tracking technology', Automation in Construction, 105: 102835.
Marsh, S. M., and D. E. Fosbroke. 2015. 'Trends of occupational fatalities involving machines, United States, 1992–2010', American journal of industrial medicine, 58: 1160–73.
Molchanov, V. V., B. V. Vishnyakov, Y. V. Vizilter, O. V. Vishnyakova, and V. A. Knyaz. 2017. “Pedestrian detection in video surveillance using fully convolutional YOLO neural network.” In Automated visual inspection and machine vision II, 193–99. SPIE.
NHTSA (National Highway Traffic Safety Administration). 1998. Drowsy driving and automobile crashes. NCSDR/NHTSA Expert Panel on Driver Fatigue and Sleepiness. In.
Niloy, A. R., A. I. Chowdhury, and N. Sharmin. 2020. 'A brief review on different Driver’s drowsiness detection techniques', International Journal of Image, Graphics and Signal Processing, 10: 41.
OSHA. 2011. Commonly used statistics: construction’s “fatal four. In.
Ramzan, M., H. U. Khan, S. M. Awan, A. Ismail, M. Ilyas, and A. Mahmood. 2019. 'A survey on state-of-the-art drowsiness detection techniques', IEEE Access, 7: 61904–19.
Redmon, J., S. Divvala, R. Girshick, and A. Farhadi. 2016. “You only look once: Unified, real-time object detection.” In Proceedings of the IEEE conference on computer vision and pattern recognition, 779–88.
Shin, D. U. K., H. Sakai, and Y. Uchiyama. 2011. 'Slow eye movement detection can prevent sleep‐related accidents effectively in a simulated driving task', Journal of sleep research, 20: 416–24.
Tan, L., T. Huangfu, L. Wu, and W. Chen. 2021. Comparison of yolo v3, faster r-cnn, and ssd for real-time pill identification.
Wang, D., H. Li, and J. Chen. 2019. 'Detecting and measuring construction workers’ vigilance through hybrid kinematic-EEG signals', Automation in Construction, 100: 11–23.
Yang, C., X. Wang, and S. Mao. 2020. 'Unsupervised drowsy driving detection with RFID', IEEE transactions on vehicular technology, 69: 8151–63.
Information & Authors
Information
Published In
Construction Research Congress 2024
Pages: 435 - 444
History
Published online: Mar 18, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Business management
- Computer networks
- Computer programming
- Computing in civil engineering
- Construction engineering
- Construction equipment
- Construction industry
- Construction management
- Employment
- Engineering fundamentals
- Equipment and machinery
- Labor
- Neural networks
- Occupational safety
- Personnel management
- Practice and Profession
- Public administration
- Public health and safety
- Safety
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.