A Real-Time Automated Approach for Ensuring Proper Use of Personal Protective Equipment (PPE) in Construction Site
A Real-Time Automated Approach for Ensuring Proper Use of Personal Protective Equipment (PPE) in Construction Site
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DOI：
10.1007/978-3-030-51295-8_77
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发表时间：
2020-08
期刊：
影响因子：
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通讯作者：
Shi Chen;K. Demachi;Manabu Tsunokai
中科院分区：
文献类型：
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作者：
Shi Chen;K. Demachi;Manabu Tsunokai
建筑工地是最危险的环境之一，可能发生许多潜在危险。尽管工人接受过远离潜在危险的培训，但仍然有许多类型的风险可能在短短几分钟内发生。个人防护装备（PPE）是用于保护建筑工人免受事故伤害的重要安全措施。然而，由于种种原因，工人个人防护用品的使用并没有得到严格执行。本文提出将基于深度学习的物体检测与使用几何关系分析的个体检测相结合，自动识别非 PPE 使用（NPU）；即，如果工人佩戴安全帽、护目镜、防尘面罩或两者，以帮助促进建筑工人的安全监控工作，以确保正确使用个人防护装备。实验结果表明，该方法能够以高精度（84.13%）和召回率（93.10%）检测NPU工作人员，同时保证实时性（平均7.95 FPS）。
Construction sites are one of the most perilous environments where many potential hazards may occur. Even though workers are trained to stay away from potential dangers, there are still many types of risks that can occur within only a few minutes of carelessness. Personal Protective Equipment (PPE) is an important safety measure used to protect construction workers from accidents. However, PPE usage is not strictly enforced among workers due to all kinds of reasons. This paper proposes the combination of deep learning-based object detection and individual detection using geometry relationships analysis to automatically identify non-PPE-use (NPU); i.e., if a worker is wearing hardhat, eye protection visors, dust masks, or both, to help to facilitate the safety monitoring work of construction workers to ensure PPE are appropriately used. The experimental results demonstrate that the approach was capable of detecting NPU workers with high precision (84.13%) and recall rate (93.10%) while ensuring real-time performance (7.95 FPS on average).