Personal Protective Equipment Detection in Extreme Construction Conditions
Publication: Computing in Civil Engineering 2023
ABSTRACT
Object detection has been widely applied for construction safety management, especially personal protective equipment (PPE) detection. Though the existing PPE detection models trained on conventional datasets have achieved excellent results, their performance dramatically declines in extreme construction conditions. A robust detection model NST-YOLOv5 is developed by combining the neural style transfer (NST) and YOLOv5 technologies. Five extreme conditions are considered and simulated via the NST module to endow the detection model with excellent robustness, including low light, intense light, sand dust, fog, and rain. Experiments show that the NST has great potential as a tool for extreme data synthesis since it is better at simulating extreme conditions than other traditional image processing algorithms and helps the NST-YOLOv5 achieve 0.141 and 0.083 mAP05:95 improvements in synthesized and real-world extreme data. This study provides a new feasible way to obtain a more robust detection model for extreme construction conditions.
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REFERENCES
Buslaev, A., V. I. Iglovikov, E. Khvedchenya, A. Parinov, M. Druzhinin, and A. A. Kalinin. 2020. “Albumentations: Fast and Flexible Image Augmentations.” Information, 11 (2). https://doi.org/10.3390/info11020125.
Ghiasi, G., H. Lee, M. Kudlur, V. Dumoulin, and J. Shlens. 2017. “Exploring the structure of a real-time, arbitrary neural artistic stylization network.” Procedings of the British Machine Vision Conference 2017, 114. London, UK: British Machine Vision Association.
Jackson, P. T., A. A. Abarghouei, S. Bonner, T. P. Breckon, and B. Obara. 2019. “Style augmentation: data augmentation via style randomization.” CVPR Workshops, 83–92.
Jocher, G., A. Stoken, A. Chaurasia, J. Borovec, NanoCode012, and TaoXie. 2021. “ultralytics/yolov5.” Zenodo.
Kang, K.-S., Y.-W. Cho, K.-H. Jin, Y.-B. Kim, and H.-G. Ryu. 2022. “Application of one-stage instance segmentation with weather conditions in surveillance cameras at construction sites.” Automation in Construction, 133: 104034. https://doi.org/10.1016/j.autcon.2021.104034.
Magenta Team. 2017. Arbitrary image stylization.
Mohammad, A., Z. Zhenhua, and H. Amin. 2020. “Nested Network for Detecting PPE on Large Construction Sites Based on Frame Segmentation.” Proceedings of the Creative Construction e-Conference 2020, 33–38. Online: Budapest University of Technology and Economics.
Nath, N. D., A. H. Behzadan, and S. G. Paal. 2020. “Deep learning for site safety: Real-time detection of personal protective equipment.” Automation in Construction, 112: 103085. https://doi.org/10.1016/j.autcon.2020.103085.
Wang, Z., Y. Wu, L. Yang, A. Thirunavukarasu, C. Evison, and Y. Zhao. 2021. “Fast Personal Protective Equipment Detection for Real Construction Sites Using Deep Learning Approaches.” Sensors, 21 (10): 3478. https://doi.org/10.3390/s21103478.
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.
Zheng, X., T. Chalasani, K. Ghosal, S. Lutz, and A. Smolic. 2019. “STaDA: Style Transfer as Data Augmentation:” Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 107–114. Prague, Czech Republic: SCITEPRESS - Science and Technology Publications.
Information & Authors
Information
Published In
Computing in Civil Engineering 2023
Pages: 672 - 679
History
Published online: Jan 25, 2024
ASCE Technical Topics:
- Business management
- Climates
- Computer vision and image processing
- Construction engineering
- Construction equipment
- Construction management
- Dust
- Engineering fundamentals
- Environmental engineering
- Equipment and machinery
- Geomechanics
- Geotechnical engineering
- Meteorology
- Methodology (by type)
- Models (by type)
- Occupational safety
- Pollutants
- Practice and Profession
- Precipitation
- Public administration
- Public health and safety
- Rainfall
- Rainfall intensity
- Safety
- Simulation models
- Soil mechanics
- Soil properties
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