One-Shot Monitoring Approach for Construction Workers’ Hardhats Based on Siamese Networks
Publication: Computing in Civil Engineering 2023
ABSTRACT
This study proposes a method to monitor workers’ personal protective equipment, particularly hardhats, at construction sites with a single query image. Prior studies on this subject required a large dataset for training, which can be time-consuming and labor-intensive since many of them utilize object detection models based on convolutional neural networks (CNNs). The proposed approach involves two steps. First, workers are detected, and their body key points are extracted through human-pose recognition used to crop head images. Second, the cropped head images are compared to a hardhat image using a Siamese network to determine whether a worker complied with the hardhat requirement. The proposed one-shot method showed 69.25% F1-score when validated against 2,000 photos of each worker wearing and not wearing a hardhat. The result indicates that the proposed approach has the potential to reduce the effort required for dataset construction while maintaining performance.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Chen, T., S. Kornblith, M. Norouzi, and G. Hinton. 2020. “A Simple Framework for Contrastive Learning of Visual Representations.”.
Cui, Z., Q. Wang, J. Guo, and N. Lu. 2022. “Few-shot classification of façade defects based on extensible classifier and contrastive learning.” Automation in Construction, 141: 104381. https://doi.org/10.1016/j.autcon.2022.104381.
Du, S., M. Shehata, and W. Badawy. 2011. “Hard hat detection in video sequences based on face features, motion and color information.” 2011 3rd International Conference on Computer Research and Development, 25–29.
Fang, Q., H. Li, X. Luo, L. Ding, H. Luo, T. M. Rose, and W. An. 2018. “Detecting non-hardhat-use by a deep learning method from far-field surveillance videos.” Automation in Construction, 85: 1–9. https://doi.org/10.1016/j.autcon.2017.09.018.
Kim, H., H. Kim, Y. W. Hong, and H. Byun. 2018. “Detecting Construction Equipment Using a Region-Based Fully Convolutional Network and Transfer Learning.” Journal of Computing in Civil Engineering, 32 (2): 04017082. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000731.
Kim, J., and S. Chi. 2021. “A few-shot learning approach for database-free vision-based monitoring on construction sites.” Automation in Construction, 124: 103566. https://doi.org/10.1016/j.autcon.2021.103566.
Koch, G., R. Zemel, and R. Salakhutdinov. 2015. “Siamese neural networks for one-shot image recognition.” ICML deep learning workshop, 0. Lille.
Konda, S., H. M. Tiesman, and A. A. Reichard. 2016. “Fatal traumatic brain injuries in the construction industry, 2003−2010.” American Journal of Industrial Medicine, 59 (3): 212–220. https://doi.org/10.1002/ajim.22557.
Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2017. “ImageNet classification with deep convolutional neural networks.” Commun. ACM, 60 (6): 84–90. https://doi.org/10.1145/3065386.
Lin, T.-Y., M. Maire, S. Belongie, L. Bourdev, R. Girshick, J. Hays, P. Perona, D. Ramanan, C. L. Zitnick, and P. Dollár. 2015. “Microsoft COCO: Common Objects in Context.”.
Park, M.-W., N. Elsafty, and Z. Zhu. 2015. “Hardhat-Wearing Detection for Enhancing On-Site Safety of Construction Workers.” Journal of Construction Engineering and Management, 141 (9): 04015024. American Society of Civil Engineers. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000974.
Redmon, J., S. Divvala, R. Girshick, and A. Farhadi. 2016. You Only Look Once: Unified, Real-Time Object Detection. 779–788.
Samaria, F. S., and A. C. Harter. 1994. “Parameterisation of a stochastic model for human face identification.” Proceedings of 1994 IEEE Workshop on Applications of Computer Vision, 138–142.
Shen, J., X. Xiong, Y. Li, W. He, P. Li, and X. Zheng. 2021. “Detecting safety helmet wearing on construction sites with bounding-box regression and deep transfer learning.” Computer-Aided Civil and Infrastructure Engineering, 36 (2): 180–196. https://doi.org/10.1111/mice.12579.
Suderman, B. L., R. W. Hoover, R. P. Ching, and I. S. Scher. 2014. “The effect of hardhats on head and neck response to vertical impacts from large construction objects.” Accident Analysis & Prevention, 73: 116–124. https://doi.org/10.1016/j.aap.2014.08.011.
Thrun, S., and L. Pratt. 2012. Learning to Learn. Springer Science & Business Media.
Wang, J., et al. 2020. “Deep High-Resolution Representation Learning for Visual Recognition.”.
Wu, J., N. Cai, W. Chen, H. Wang, and G. Wang. 2019. “Automatic detection of hardhats worn by construction personnel: A deep learning approach and benchmark dataset.” Automation in Construction, 106: 102894. https://doi.org/10.1016/j.autcon.2019.102894.
Xie, L. 2019. Hardhat. Harvard Dataverse.
Information & Authors
Information
Published In
Computing in Civil Engineering 2023
Pages: 525 - 531
History
Published online: Jan 25, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Business management
- Computer programming
- Computing in civil engineering
- Construction engineering
- Construction equipment
- Construction management
- Construction methods
- Construction sites
- Education
- Employment
- Engineering fundamentals
- Equipment and machinery
- Human and behavioral factors
- Labor
- Neural networks
- Occupational safety
- Personnel management
- Practice and Profession
- Public administration
- Public health and safety
- Safety
- Training
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.