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Journals / Bitlis Eren Üniversitesi Fen Bilimleri Dergisi / 2023 / Cilt: 12 Sayı: 3

Deep Learning-Based Automatic Helmet Detection System in Construction Site Cameras

Adem KORKMAZ
Mehmet Tevfik AĞDAŞ
Pages
773–782
Publication Date
2023-09-28
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results demonstrate the effectiveness of the YOLOv8 model for accurate and efficient helmet detection in construction sites, paving the way for improved safety monitoring and enforcement in the construction industry.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Ensuring worker safety in high-risk environments such as construction sites is of paramount importance.
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Key Findings & Data:This study aims to develop an automated helmet detection system using the state-of-the-art YOLOv8 deep learning model to enhance safety monitoring in real-time.
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Conclusion & Impact:The results demonstrate the effectiveness of the YOLOv8 model for accurate and efficient helmet detection in construction sites, paving the way for improved safety monitoring and enforcement in the construction industry.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Helmet detection · Object detection · YOLOv8

Abstract

Ensuring worker safety in high-risk environments such as construction sites is of paramount importance. Personal protective equipment, particularly helmets, plays a critical role in preventing severe head injuries. This study aims to develop an automated helmet detection system using the state-of-the-art YOLOv8 deep learning model to enhance safety monitoring in real-time. The dataset used for the study consists of 16,867 images, with various data augmentation and preprocessing techniques applied to improve the model's robustness. The YOLOv8 model achieved a 96.9% mAP50 score, outperforming other deep learning models in similar studies. The results demonstrate the effectiveness of the YOLOv8 model for accurate and efficient helmet detection in construction sites, paving the way for improved safety monitoring and enforcement in the construction industry.

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Cite This Article

KORKMAZ, A., AĞDAŞ, M. T. (2023). Deep Learning-Based Automatic Helmet Detection System in Construction Site Cameras. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, Cilt: 12 Sayı: 3, 773–782. https://ajindex.com/articles/966010

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References (13)

  1. X. Huang and J. Hinze, “Analysis of construction worker fall accidents,” J. Constr. Eng. Manag., vol. 129, no. 3, pp. 262–271, 2003
  2. R. A. Haslam et al., “Contributing factors in construction accidents,” Appl. Ergon., vol. 36, no. 4, pp. 401–415, 2005
  3. A. Hayat and F. Morgado-Dias, “Deep learning-based automatic safety helmet detection system for construction safety,” Appl. Sci. (Basel), vol. 12, no. 16, p. 8268, 2022.
  4. S. Tan, G. Lu, Z. Jiang, and L. Huang, “Improved YOLOv5 network model and application in safety helmet detection,” in 2021 IEEE International Conference on Intelligence and Safety for Robotics (ISR), 2021.
  5. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015. doi:10.1038/nature14539 [6] J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” arXiv [cs.CV], 2018
  6. R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014
  7. P. Doungmala and K. Klubsuwan, “Helmet wearing detection in Thailand using Haar like feature and circle Hough transform on image processing,” in 2016 IEEE International Conference on Computer and Information Technology (CIT), 2016
  8. W. Zhang, C.-F. Yang, F. Jiang, X.-Z. Gao, and X. Zhang, “Safety helmet wearing detection based on image processing and deep learning,” in 2020 International Conference on Communications, Information System and Computer Engineering (CISCE), 2020
  9. S. H. Kim, C. Wang, S. D. Min, and S. H. Lee, “Safety helmet wearing management system for construction workers using three-axis accelerometer sensor,” Appl. Sci. (Basel), vol. 8, no. 12, p. 2400, 2018
  10. X. Long, W. Cui, and Z. Zheng, “Safety helmet wearing detection based on deep learning,” in 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 2019
  11. Y. Li, H. Wei, Z. Han, J. Huang, and W. Wang, “Deep learning-based safety helmet detection in engineering management based on convolutional neural networks,” Advances in Civil Engineering, vol. 2020, pp. 1–10, 2020
  12. H. Wang, Z. Hu, Y. Guo, Z. Yang, F. Zhou, and P. Xu, “A real-time safety helmet wearing detection approach based on CSYOLOv3,” Appl. Sci. (Basel), vol. 10, no. 19, p. 6732, 2020
  13. L. Huang, Q. Fu, M. He, D. Jian