YOLOv11-Based Automated PPE Detection System for Workplace Safety Monitoring in Electric Power Distribution Operations

Authors

  • Jevon Ordrick Banyuwangi State Polytechnic, Indonesia
  • Galih Hendra Wibowo Banyuwangi State Polytechnic, Indonesia
  • Arif Fahmi Banyuwangi State Polytechnic, Indonesia
  • Indra Kurniawan Banyuwangi State Polytechnic, Indonesia
  • Endi Sailul Haq Banyuwangi State Polytechnic, Indonesia
Pages Icon

DOI:

https://doi.org/10.63158/journalisi.v7i4.1379

Keywords:

Personal Protective Equipment (PPE), YOLOv11, Deep Learning, Computer Vision, Workplace Safety

Abstract

Manual monitoring of Personal Protective Equipment (PPE) compliance in electric power distribution is prone to human error, limited supervision, and geographically dispersed work sites. This study proposes an automated PPE detection system using the YOLOv11 deep learning model to enhance safety monitoring at PT PLN (Persero) UP3 Banyuwangi. A dataset of 589 images containing 1,425 labeled PPE instances across seven categories was used to train the YOLOv11s model. The system was deployed via a web-based application with adjustable detection thresholds and validated through interviews with three OHS supervisors. It achieved 94.0% precision, 90.1% recall, and 92.8% mAP@50, with perfect detection for persons and near-perfect results for full-body harnesses. The application processed images in 2–3 seconds on standard CPU hardware, supporting automated documentation for compliance reporting. This is the first known YOLOv11-based PPE detection system tailored to electric power distribution settings. While results are promising, limitations include a small validation set and lower accuracy in detecting safety boots. Future work should explore real-time video analysis, system integration, and long-term studies on safety compliance improvements.

Downloads

Download data is not yet available.

References

B. I. Arianto, "Implementasi Metode YOLO pada Deteksi Pakaian Keselamatan yang Lengkap di Proyek Kontruksi," Ranah Research: Journal of Multidisciplinary Research and Development, vol. 6, no. 1, pp. 56–63, 2023.

M. Ferdous and S. M. M. Ahsan, "PPE detector: a YOLO-based architecture to detect personal protective equipment (PPE) for construction sites," PeerJ Computer Science, vol. 8, p. e999, Jun. 2022, doi: 10.7717/peerj-cs.999.

K. S. Park and M. Lee, "Framework of automated construction-safety monitoring using cloud-enabled BIM and BLE mobile tracking sensors," Journal of Construction Engineering and Management, vol. 143, no. 2, p. 05016019, Feb. 2017, doi: 10.1061/(ASCE)CO.1943-7862.0001223.

M. Guo, Y. Liu, and J. Malec, "A new Q&A pair generation method from online forum using deep learning," Expert Systems with Applications, vol. 151, p. 113350, Aug. 2020, doi: 10.1016/j.eswa.2020.113350.

A. Fang, D. Wu, and J. Li, "Construction worker safety analysis using computer vision: A review," Safety Science, vol. 149, p. 105681, May 2022, doi: 10.1016/j.ssci.2022.105681.

W. Fang, L. Ding, H. Luo, and P. E. D. Love, "Falls from heights: A computer vision-based approach for safety harness detection," Automation in Construction, vol. 91, pp. 53–61, Jul. 2018, doi: 10.1016/j.autcon.2018.02.018.

Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436–444, May 2015, doi: 10.1038/nature14539.

K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 770–778, doi: 10.1109/CVPR.2016.90.

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: Unified, real-time object detection," in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 779–788, doi: 10.1109/CVPR.2016.91.

Z. Wang, Y. Wu, L. Yang, A. Thirunavukarasu, C. Evison, and Y. Zhao, "Fast personal protective equipment detection for real construction sites using deep learning approaches," Sensors, vol. 21, no. 10, p. 3478, May 2021, doi: 10.3390/s21103478.

Z. S. Juanda, Z. K. Simbolon, and H. Huzaeni, "Mengintegrasikan Metode YOLO (You Only Look Once) Dalam Deteksi APD (Alat Pelindung Diri) Pada Industri Migas," Journal of TIK, vol. 4, no. 1, pp. 127–135, 2024, doi: 10.33365/jtik.v4i1.2156.

S. Malaikrisanachalee, N. Wongwai, and E. Kowcharoen, "ESPCN-YOLO: A High-Accuracy Framework for Personal Protective Equipment Detection Under Low-Light and Small Object Conditions," Buildings, vol. 15, no. 10, p. 1609, May 2025, doi: 10.3390/buildings15101609.

D. Ocharo, H. Nganga, and S. Kiambi, "Automatic PPE monitoring system for construction workers using YOLO algorithm based on deep reinforcement learning," International Journal of Computer Applications, vol. 186, no. 12, pp. 10–15, Mar. 2024, doi: 10.5120/ijca2024923512.

N. Z. A. A. Rahmah, R. Indarti, Z. M. A. Putra, E. Setiawan, and A. Z. Arfianto, "Implementasi Deteksi Kelengkapan APD pada Hazardous Area menggunakan Metode YoloV5," Jurnal Elektronika dan Otomasi Industri, vol. 11, no. 3, pp. 245–253, Dec. 2024, doi: 10.33795/elkolind.v11i3.485.

D. Tan and S. Prayogi, "Deteksi Otomatis Penggunaan APD pada Pekerja Migas Menggunakan Deep Learning dan Computer Vision," Techno.com, vol. 24, no. 3, pp. 412–425, Aug. 2025, doi: 10.33633/tc.v24i3.9234.

M. A. Taufiqurrochman and H. Februariyanti, "Rancang Bangun Aplikasi Deteksi Alat Pelindung Diri (APD) untuk Pekerja Proyek dengan Menggunakan Algoritma Yolov5," Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), vol. 8, no. 2, pp. 471–480, Apr. 2024, doi: 10.35870/jtik.v8i2.1245.

B. Tong, G. Li, X. Bu, Y. Wang, and X. Yu, "A deep learning-based algorithm for the detection of personal protective equipment," PLOS ONE, vol. 20, no. 5, p. e0322115, May 2025, doi: 10.1371/journal.pone.0322115.

N. D. Nath, A. H. Behzadan, and S. G. Paal, "Deep learning for site safety: Real-time detection of personal protective equipment," Automation in Construction, vol. 112, p. 103085, Apr. 2020, doi: 10.1016/j.autcon.2020.103085.

H. Wu, J. Zhao, and W. Yan, "Real-time helmet detection based on improved YOLOv4," in Proc. Chinese Control and Decision Conference (CCDC), Hefei, China, 2021, pp. 2189–2194, doi: 10.1109/CCDC52312.2021.9601518.

K. Kaplan, Y. Kaya, M. Kuncan, and H. M. Ertunç, "An Improved Feature Extraction Method Using Texture Analysis with LBP for Bear Recognition," in Proc. Int. Conf. Artificial Intelligence and Data Processing (IDAP), Malatya, Turkey, 2018, pp. 1–4, doi: 10.1109/IDAP.2018.8620808.

J. Li, H. Liu, T. Wang, M. Jiang, S. Wang, K. Li, and X. Zhao, "Safety helmet wearing detection based on image processing and machine learning," in Proc. Int. Conf. Advanced Computational Intelligence (ICACI), Guilin, China, 2019, pp. 174–179, doi: 10.1109/ICACI.2019.8778583.

H. Zhang, X. Yan, H. Li, R. Jin, and D. Fu, "Real-time alarming, monitoring, and locating for non-hard-hat use in construction," Journal of Construction Engineering and Management, vol. 145, no. 3, p. 04019006, Mar. 2019, doi: 10.1061/(ASCE)CO.1943-7862.0001629.

B. E. Mneymneh, M. Abbas, and H. Khoury, "Vision-based framework for intelligent monitoring of hardhat wearing on construction sites," Journal of Computing in Civil Engineering, vol. 33, no. 2, p. 04018066, Mar. 2019, doi: 10.1061/(ASCE)CP.1943-5487.0000813.

A. Kelm, L. Laußat, A. Meins-Becker, D. Platz, M. Khazaee, and A. C. Costin, "Mobile passive Radio Frequency Identification (RFID) portal for automated and rapid control of Personal Protective Equipment (PPE) on construction sites," Automation in Construction, vol. 36, pp. 38–52, Dec. 2013, doi: 10.1016/j.autcon.2013.08.009.

M. W. Park, N. Elsafty, and Z. Zhu, "Hardhat-wearing detection for enhancing on-site safety of construction workers," Journal of Construction Engineering and Management, vol. 141, no. 9, p. 04015024, Sep. 2015, doi: 10.1061/(ASCE)CO.1943-7862.0000974.

Q. Fang, H. Li, X. Luo, L. Ding, H. Luo, T. M. Rose, and W. An, "Detecting non-hardhat-use by a deep learning method from far-field surveillance videos," Automation in Construction, vol. 85, pp. 1–9, Jan. 2018, doi: 10.1016/j.autcon.2017.09.018.

C. Y. Wang, A. Bochkovskiy, and H. Y. M. Liao, "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors," in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada, 2023, pp. 7464–7475, doi: 10.1109/CVPR52729.2023.00721.

Downloads

Published

2025-12-26

Issue

Vol. 7 No. 4 (2025): December

Section

Articles

License

Authors Declaration

  1. The Authors certify that they have read, understood, and agreed to the Journal of Information Systems and Informatics (JournalISI) submission guidelines, policies, and submission declaration. The submission has been prepared using the provided template.
  2. The Authors certify that all authors have approved the publication of this manuscript and that there is no conflict of interest.
  3. The Authors confirm that the manuscript is their original work, has not received prior publication, is not under consideration for publication elsewhere, and has not been previously published.
  4. The Authors confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
  5. The Authors confirm that the manuscript is not copied from or plagiarized from any other published work.
  6. The Authors declare that the manuscript will not be submitted for publication in any other journal or magazine until a decision is made by the journal editors.
  7. If the manuscript is finally accepted for publication, the Authors confirm that they will either proceed with publication immediately or withdraw the manuscript in accordance with the journal’s withdrawal policies.
  8. The Authors agree that, upon publication of the manuscript in this journal, they transfer copyright or assign exclusive rights to the publisher, including commercial rights

How to Cite

[1]
J. Ordrick, G. H. Wibowo, A. Fahmi, I. Kurniawan, and E. S. Haq, “YOLOv11-Based Automated PPE Detection System for Workplace Safety Monitoring in Electric Power Distribution Operations”, journalisi, vol. 7, no. 4, pp. 4294–4329, Dec. 2025, doi: 10.63158/journalisi.v7i4.1379.