YOLO-Based Personal Protective Equipment Monitoring System for Workplace Safety
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Published:
Jun 30, 2024
Keywords:
YOLO-NAS; YOLOv8; YOLOv9; PPE; Safety Helmet Detection
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Abstract
Occupational health and safety are of paramount importance in industrial environments and various work fields. In this context, tracking personal protective equipment (PPE) is highly necessary. This article investigates the performance and application of deep learning-based object detection models to enhance the accuracy and speed of tracking personal protective equipment for ensuring occupational health and safety. These models detect personal protective equipment in images, enabling monitoring of their correct usage and intervention when necessary. The study aims to minimize damage resulting from accidents through the use of protective equipment and to prevent possible accidents. In our study, a dataset consisting of 2581 images, encompassing different workplace environments and workers, was prepared. This dataset was evaluated for performance using deep learning models. Popular deep-learning models such as YOLO-NAS, YOLOv8, and YOLOv9 were utilized in the comparisons. During the training of the models, the number of epochs was kept consistent for fair comparison. Upon examining the results, it is observed that the YOLO-NAS and YOLOv9 models generally exhibit similar and high performance.
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How to Cite
Guney, E., Altin, H., Esra Asci, A., Bayilmis, O. U., & Bayilmis, C. (2024). YOLO-Based Personal Protective Equipment Monitoring System for Workplace Safety. JITSI : Jurnal Ilmiah Teknologi Sistem Informasi, 5(2), 77 - 85. https://doi.org/10.62527/jitsi.5.2.238
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References
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[5] Hayat, A., Morgado-Dias, F.: Deep learning-based automatic safety helmet detection system for construction safety. Applied Sciences, 12.16: 8268, (2022)
[6] Türkdamar, M., Taşyürek, M., Öztürk C.: Transfer öğrenmeli ve transfer öğrenmesiz derin ağlar ile inşaat alanında kask tespiti[ Helmet detection in the construction site with deep networks with and without transfer learning].Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi[Niğde Ömer Halisdemir University Journal of Engineering Sciences], 12.1: 39-51, (2023)
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[12] Aboah, A., Wang, B., Bagci, U., Adu-Gyamfi, Y.: Real-time Multi-Class Helmet Violation Detection Using Few-Shot Data Sampling Technique and YOLOv8. In Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2023, pp. 5350-5358. IEEE/CVF (2023)
[13] Chen, W., Huang, H., Peng, S.: YOLO-face: a real-time face detector. Vis Comput 37, 805–813 (2021)
[14] Nath,N., Behzadan, A., Paal,S.: Deep learning for site safety: Real-time detection of personal protective equipment. Automation in Construction 112, 103085(2020)
[15] Gallo, G., Di Rienzo, F., Ducange, P., Ferrari, V., Tognetti, A., Vallati, C.: A Smart System for Personal Protective Equipment Detection in Industrial Environments Based on Deep Learning. In: International Conference on Smart Computing (SMARTCOMP 2021, pp. 222-227. IEEE (2021)
[16] Zhou, Q., Qin, J., Xiang, X.: Algorithm of Helmet Wearing Detection Based on AT-YOLO Deep Mode. Computers, Materials & Continua 69(1), 159–174 (2021)
[17] Zeng, L., Duan, X., Pan, Y.: Research on the algorithm of helmet-wearing detection based on the optimized yolov4. Vis Comput 39, 2165–2175 (2023)
[18] Yung, Yung, N., Juwono, F., Wong, W., Sim,Z.: Safety Helmet Detection Using Deep Learning: Implementation and Comparative Study Using YOLOv5, YOLOv6, and YOLOv7. In: International Conference on Green Energy, Computing and Sustainable Technology (GECOST) 2022, pp. 164–169., IEEE, (2023)
[19] Yung, Yung, N., Juwono, F., Wong, W., Sim,Z.: Safety Helmet Detection Using Deep Learning: Implementation and Comparative Study Using YOLOv5, YOLOv6, and YOLOv7. In: International Conference on Green Energy, Computing and Sustainable Technology (GECOST) 2022, pp. 164–169., IEEE, (2023)
[20] Guney, E., Sahin, I. H., Cakar, S., Atmaca, O., Erol, E., Doganli, M., and Bayilmis, C.: Electric shore-to-ship charging socket detection using image processing and YOLO. In 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 1069-1073. (2022)
[21] Güney, E., Bayılmış, C., Çakar, S., Erol, E., & Atmaca, Ö.: Autonomous control of shore robotic charging systems based on computer vision. Expert Systems with Applications, pp. 122116. (2023)
[22] Güney, E., & Bayılmış, C.: An implementation of traffic signs and road objects detection using faster R-CNN. Sakarya University Journal of Computer and Information Sciences, 5(2), pp. 216-224. (2022)
[2] Eurostat. (n.d.). "Fatal and non-fatal accidents at work by type of injury." Statistics Explained. European Commission. Accessed: [Insert access date]. URL: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=File:Fatal_and_non-fatal_accidents_at_work_by_type_of_injury.png
[3] Kilkiş, İ.: İş Sağlığı ve Güvenliği [Occupational Health and Safety]. (4th ed.).: Dora Yayıncılık, Bursa, Turkey (2022)
[4] Girgin, İ.: Makine Öğrenmesi ile İş Sağlığı ve Güvenliği Uygulamaları (Applications of Machine Learning in Occupational Health and Safety] (master’s thesis). Ankara Üniversitesi [Ankara Universitiy] (2023)
[5] Hayat, A., Morgado-Dias, F.: Deep learning-based automatic safety helmet detection system for construction safety. Applied Sciences, 12.16: 8268, (2022)
[6] Türkdamar, M., Taşyürek, M., Öztürk C.: Transfer öğrenmeli ve transfer öğrenmesiz derin ağlar ile inşaat alanında kask tespiti[ Helmet detection in the construction site with deep networks with and without transfer learning].Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi[Niğde Ömer Halisdemir University Journal of Engineering Sciences], 12.1: 39-51, (2023)
[7] Wang, Z., Wu, Y., Yang, L., Thirunavukarasu, A., Evison, C., Zhao, Y.: Fast personal protective equipment detection for real construction sites using deep learning approaches. Sensors, 21.10: 3478, (2021)
[8] Delhi, V., Sankarlal R., Thomas, A.: Detection of personal protective equipment (PPE) compliance on construction site using computer vision based deep learning techniques. Frontiers in Built Environment. 6: 136, (2020)
[9] Anushkannan, N., Kumbhar, V., Maddila, S., Kolli, C., Vidhya, B., Vidhya, R.: YOLO algorithm for helmet detection in industries for safety purpose. In 3rd International Conference on Smart Electronics and Communication (ICOSEC) 2022, pp. 225-230. IEEE (2022)
[10] Zhou, F., Zhao, H., Nie, Z.: Safety Helmet Based on YOLOv5. In International Conference on Power Electronics, Computer Applications (ICPECA) 2021, pp. 6-11. IEEE (2021)
[11] Ferdous, M., Ahsan, SMM.: PPE detector: a YOLO-based architecture to detect personal protective equipment (PPE) for construction sites. PeerJ Computer Science 8(999), (2022)
[12] Aboah, A., Wang, B., Bagci, U., Adu-Gyamfi, Y.: Real-time Multi-Class Helmet Violation Detection Using Few-Shot Data Sampling Technique and YOLOv8. In Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2023, pp. 5350-5358. IEEE/CVF (2023)
[13] Chen, W., Huang, H., Peng, S.: YOLO-face: a real-time face detector. Vis Comput 37, 805–813 (2021)
[14] Nath,N., Behzadan, A., Paal,S.: Deep learning for site safety: Real-time detection of personal protective equipment. Automation in Construction 112, 103085(2020)
[15] Gallo, G., Di Rienzo, F., Ducange, P., Ferrari, V., Tognetti, A., Vallati, C.: A Smart System for Personal Protective Equipment Detection in Industrial Environments Based on Deep Learning. In: International Conference on Smart Computing (SMARTCOMP 2021, pp. 222-227. IEEE (2021)
[16] Zhou, Q., Qin, J., Xiang, X.: Algorithm of Helmet Wearing Detection Based on AT-YOLO Deep Mode. Computers, Materials & Continua 69(1), 159–174 (2021)
[17] Zeng, L., Duan, X., Pan, Y.: Research on the algorithm of helmet-wearing detection based on the optimized yolov4. Vis Comput 39, 2165–2175 (2023)
[18] Yung, Yung, N., Juwono, F., Wong, W., Sim,Z.: Safety Helmet Detection Using Deep Learning: Implementation and Comparative Study Using YOLOv5, YOLOv6, and YOLOv7. In: International Conference on Green Energy, Computing and Sustainable Technology (GECOST) 2022, pp. 164–169., IEEE, (2023)
[19] Yung, Yung, N., Juwono, F., Wong, W., Sim,Z.: Safety Helmet Detection Using Deep Learning: Implementation and Comparative Study Using YOLOv5, YOLOv6, and YOLOv7. In: International Conference on Green Energy, Computing and Sustainable Technology (GECOST) 2022, pp. 164–169., IEEE, (2023)
[20] Guney, E., Sahin, I. H., Cakar, S., Atmaca, O., Erol, E., Doganli, M., and Bayilmis, C.: Electric shore-to-ship charging socket detection using image processing and YOLO. In 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 1069-1073. (2022)
[21] Güney, E., Bayılmış, C., Çakar, S., Erol, E., & Atmaca, Ö.: Autonomous control of shore robotic charging systems based on computer vision. Expert Systems with Applications, pp. 122116. (2023)
[22] Güney, E., & Bayılmış, C.: An implementation of traffic signs and road objects detection using faster R-CNN. Sakarya University Journal of Computer and Information Sciences, 5(2), pp. 216-224. (2022)