SentryNap: Sistem Peringatan dan Monitoring Kantuk Operator Industri Menggunakan YOLOv5 dan CCTV

M Ilham Yusuf Gumai; Yohana Christy Relyana Sembiring; Sri Lestari

doi:10.30591/jpit.v11i2.10111

SentryNap: Sistem Peringatan dan Monitoring Kantuk Operator Industri Menggunakan YOLOv5 dan CCTV

M Ilham Yusuf Gumai, Yohana Christy Relyana Sembiring, Sri Lestari

Abstract

This study aims to develop SentryNap, a real-time drowsiness warning and monitoring system for industrial control-room operators based on YOLOv5s using CCTV/webcam image input. Reduced alertness caused by long shift work and operator drowsiness can increase the risk of operational errors, while many previous approaches still rely on intrusive physiological sensors or visual methods that are sensitive to changes in lighting and head pose. The proposed system uses a YOLOv5s model fine-tuned on a Roboflow dataset with two classes, namely normal and sleeping, and integrates the inference results with a Node.js backend for JSON logging. Model training was conducted for 50 epochs at a resolution of 640 x 640 pixels using the SGD optimizer, while evaluation was carried out through static validation and real-time testing scenarios. The model achieved a precision of 0.986, recall of 1.000, [email protected] of 0.995, and [email protected]:0.95 of 0.621. Real-time testing showed that detection results could be recorded by the backend in less than one second. These findings indicate that SentryNap has potential as a non-invasive operator safety monitoring prototype, although larger datasets and broader field validation are still required.

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References

T. Mentler, T. Rasim, M. Müßiggang, and M. Herczeg, “Ensuring usability of future smart energy control room systems,” Energy Informatics, vol. 1, pp. 167–182, Oct. 2018, doi: 10.1186/s42162-018-0029-z.

S. Folkard, D. A. Lombardi, and P. T. Tucker, “Shiftwork: Safety, Sleepiness and Sleep,” 2005.

R. Seibt, S. Kreuzfeld, and B. Hunger, “Effects of a 12-hour shift system on sleep and cardiovascular health of male machine and plant operators – a longitudinal study over four years,” Front. Public Health, vol. 13, no. August, pp. 1–20, 2025, doi: 10.3389/fpubh.2025.1616810.

H. Zidny Ilmadina, D. Apriliani, and D. S. Wibowo, “Deteksi Pengendara Mengantuk dengan Kombinasi Haar Cascade Classifier dan Support Vector Machine,” vol. 7, no. 1, 2022.

H. Elmoaqet, M. Eid, M. Glos, M. Ryalat, and T. Penzel, “Deep recurrent neural networks for automatic detection of sleep apnea from single channel respiration signals,” Sensors (Switzerland), vol. 20, no. 18, pp. 1–19, Sep. 2020, doi: 10.3390/s20185037.

N. Sridhar et al., “Deep learning for automated sleep staging using instantaneous heart rate,” NPJ Digit. Med., vol. 3, no. 1, Dec. 2020, doi: 10.1038/s41746-020-0291-x.

P. Somaskandhan et al., “Deep learning-based algorithm accurately classifies sleep stages in preadolescent children with sleep-disordered breathing symptoms and age-matched controls,” Front. Neurol., vol. 14, 2023, doi: 10.3389/fneur.2023.1162998.

Z. AlArnaout, C. Zaki, Y. Kotb, M. AlAkkoumi, and N. Mostafa, “Exploiting heart rate variability for driver drowsiness detection using wearable sensors and machine learning,” Sci. Rep., vol. 15, no. 1, pp. 1–24, 2025, doi: 10.1038/s41598-025-08582-2.

Y. Albadawi, A. AlRedhaei, and M. Takruri, “Real-Time Machine Learning-Based Driver Drowsiness Detection Using Visual Features,” J. Imaging, vol. 9, no. 5, 2023, doi: 10.3390/jimaging9050091.

H. Chen, Z. Chen, and H. Yu, “Enhanced YOLOv5: An Efficient Road Object Detection Method,” Sensors (Basel), vol. 23, no. 20, Oct. 2023, doi: 10.3390/s23208355.

X. Ran, S. He, and R. Li, “Research on Fatigued-Driving Detection Method by Integrating Lightweight YOLOv5s and Facial 3D Keypoints,” Sensors, vol. 23, no. 19, 2023, doi: 10.3390/s23198267.

B. Kanigoro and B. Asdyo, “Facial Landmark and YOLOv5 Drowsiness Detection System,” in Procedia Computer Science, Elsevier B.V., 2024, pp. 548–554. doi: 10.1016/j.procs.2024.10.281.

S. Essahraui et al., “Real-Time Driver Drowsiness Detection Using Facial Analysis and Machine Learning Techniques,” Sensors, vol. 25, no. 3, Feb. 2025, doi: 10.3390/s25030812.

S. Priyanka, S. Shanthi, A. Saran Kumar, and V. Praveen, “Data fusion for driver drowsiness recognition: A multimodal perspective,” Egyptian Informatics Journal, vol. 27, p. 100529, Sep. 2024, doi: 10.1016/J.EIJ.2024.100529.

O. F. Hassan, A. F. Ibrahim, A. Gomaa, M. A. Makhlouf, and B. Hafiz, “Real-time driver drowsiness detection using transformer architectures: a novel deep learning approach,” Sci. Rep., vol. 15, no. 1, pp. 1–33, 2025, doi: 10.1038/s41598-025-02111-x.

J. R. Theivadas and S. Ponnan, “VigilEye: Machine learning-powered driver fatigue recognition for safer roads,” Measurement: Sensors, vol. 33, no. May, p. 101186, 2024, doi: 10.1016/j.measen.2024.101186.

M. Venkateswarlu and V. Rami Reddy Ch, “DrowsyDetectNet: Driver Drowsiness Detection Using Lightweight CNN With Limited Training Data,” IEEE Access, vol. 12, no. August, pp. 110476–110491, 2024, doi: 10.1109/ACCESS.2024.3440585.

P. Yuan, C. Fan, and C. Zhang, “YOLOv5s-MEE: A YOLOv5-based Algorithm for Abnormal Behavior Detection in Central Control Room,” Information Technology and Control, vol. 53, no. 1, pp. 220–236, Mar. 2024, doi: 10.5755/j01.itc.53.1.33458.

G. S. Lozano-Reyes and C. A. Mugruza-Vassallo, “A vision-based drowsiness detection system for railway operators using lightweight convolutional neural networks,” Frontiers in Future Transportation, vol. 6, no. November, pp. 1–10, 2025, doi: 10.3389/ffutr.2025.1677442.

A. Rehman, M. Z. Waseem, A. Rafey, A. A. Hussaini, H. Parveen, and N. Khan, “Identification Detection and YoloV5 based Driver Drowsiness Framework,” Int. J. Innov. Sci. Res. Technol., pp. 2806–2815, May 2025, doi: 10.38124/ijisrt/25apr1476.

Prof. B. S S, R. Kolhe, N. K. Pingale, and D. Singh Chandel, “Drowsiness Detection System: Integrating YOLOv5 Object Detection with Arduino Hardware for Real-Time Monitoring,” International Journal of Innovative Research in Computer Science and Technology, vol. 12, no. 2, pp. 59–66, Mar. 2024, doi: 10.55524/ijircst.2024.12.2.9.

Y. Disha and A. Upadhyaya, “A YOLO and Machine Learning-Based Framework for Real-Time Driver Drowsiness Detection,” 2025.

S. Srivastava, A. V. Divekar, C. Anilkumar, I. Naik, V. Kulkarni, and V. Pattabiraman, “Comparative analysis of deep learning image detection algorithms,” J. Big Data, vol. 8, no. 1, 2021, doi: 10.1186/s40537-021-00434-w.

L. Ying, Z. Lei, G. Junwei, H. Jinhui, M. Lei, and Z. Zilong, “Unsafe behaviour detection with the improved YOLOv5 model,” IET Cyber-Physical Systems: Theory and Applications, vol. 9, no. 1, pp. 87–98, Mar. 2024, doi: 10.1049/cps2.12070.

Y. Liu, P. Wang, and H. Li, “An Improved YOLOv5s-Based Algorithm for Unsafe Behavior Detection of Construction Workers in Construction Scenarios,” Applied Sciences (Switzerland), vol. 15, no. 4, Feb. 2025, doi: 10.3390/app15041853.

DOI: https://doi.org/10.30591/jpit.v11i2.10111