Deep learning-based framework for monitoring wearing personal protective equipment on construction sites

نویسندگان

چکیده

Abstract The construction site is one of the most dangerous industries because number occupational injuries and fatalities significantly higher compared to other industries. Proper use personal protective equipment (PPE) by workers can reduce risk fatalities. However, for a variety reasons, tend not wear their PPEs properly. To address these issues, we propose vision-based framework monitoring wearing PPE. developed based on real-time pixel-level detect model YOLACT, which employs MobileNetV3 as backbone lightweight proposed framework. In addition, uses DeepSORT object tracking algorithm interpolate frames predicted model. post-processing in our classifies correlation between PPE into four statuses results YOLACT interpolated from DeepSORT. showed that fine-tuned achieved 66.4 mean average precision50, successfully determined workers’ PPE-wearing status detection with 91.3% accuracy. This study shows potential prevent social costs automating at sites real-time.

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ژورنال

عنوان ژورنال: Journal of Computational Design and Engineering

سال: 2023

ISSN: ['2288-5048', '2288-4300']

DOI: https://doi.org/10.1093/jcde/qwad019