Pose guided anchoring for detecting proper use of personal protective equipment
Pose guided anchoring for detecting proper use of personal protective equipment
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DOI：
10.1016/j.autcon.2021.103828
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发表时间：
2021-10
影响因子：
10.3
通讯作者：
Ruoxin Xiong;P. Tang
中科院分区：
文献类型：
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作者：
Ruoxin Xiong;P. Tang
确保正确使用个人防护设备（PPE）对于改善工作场所安全管理至关重要。作者提出了一个可扩展的姿势引导锚定框架，旨在多类PPE合规性检测。整体方法利用姿态估计器来检测工人身体部位作为空间锚点，并使用基于身体知识的规则考虑工人的方向和对象尺度来指导部分注意区域的定位。具体地，“部位关注区域”是基于它们与身体部位的固有关系而期望PPE的局部图像块，例如，(head、安全帽）和（上身、背心）。最后，基于CNN的浅层分类器可以可靠地识别PPE和非PPE类在其相应的部分注意区域。定量评估测试开发的建筑个人防护设备数据集（CPPE）显示整体0.97和0.95 F1分数分别为安全帽和安全背心检测。与现有方法的比较研究也表明了更高的检测精度和所提出的策略的可扩展性。
Ensuring proper use of personal protective equipment (PPE) is essential for improving workplace safety management. The authors present an extensible pose-guided anchoring framework aimed at multi-class PPE compliance detection. The overall approach harnesses a pose estimator to detect worker body parts as spatial anchors and guide the localization of part attention regions using body-knowledge-based rules considering workers' orientations and object scales. Specifically, “part attention regions” are local image patches expecting PPEs based on their inherent relationships with body parts, e.g., (head, hardhat) and (upper-body, vest). Finally, the shallow CNN-based classifiers can reliably recognize both PPE and non-PPE classes within their corresponding part attention regions. Quantitative evaluations tested on the developed construction personal protective equipment dataset (CPPE) show an overall 0.97 and 0.95 F1-score for hardhat and safety vest detection, respectively. Comparative studies with existing methods also demonstrate the higher detection accuracy and advantageous extensibility of the proposed strategy.