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Generic compliance of industrial PPE by using deep learning techniques
Safety Science ( IF 6.1 ) Pub Date : 2021-12-29 , DOI: 10.1016/j.ssci.2021.105646
Arso M. Vukicevic 1 , Marko Djapan 1 , Velibor Isailovic 1 , Danko Milasinovic 2 , Marija Savkovic 1 , Pavle Milosevic 3
Affiliation  

Inability of safety managers to timely detect misuse of Personal protective equipment (PPE) causes a number of injuries and financial losses. Considering sizes of industry halls and number of workers, there is an increasing demand for computerized tools that could help companies to enhance the implementation of strictinging workplace safety standards. As a solution, we propose a procedure that: 1) reduces the problem of PPE compliance to the binary classification, and 2) enables compliance of arbitrary type and number of PPE that could be mounted on various body parts. To prove this hypothesis, we studied 18 different PPE types used across various industries for protecting 5 physiological body parts/functions. The HigherHRNet pose estimator was used for defining the PPE regions of interest, while six different image classification architectures were assessed for the compliance/classification of the considered regions. All classifiers were pretrained on the ImageNet data set and fine-tuned using the dedicated data set developed during this study. Top-performing models were MobileNetV2, Dense-Net, and ResNet, while the MobileNetV2 was recommended as the most optimal choice considering its lower computation demands. Compared to previous studies, the proposed approach demonstrated competing performances with unique ability to be easily adopted for performing compliance of various PPE by slight editing of the predefined lists of PPE types and corresponding body parts. Considering the present data/privacy/computational constraints, the procedure is recommended as suited for the digitalization of PPE compliance in: 1) self-check points, and 2) safety-critical workplaces.



中文翻译:

使用深度学习技术实现工业 PPE 的通用合规性

安全管理人员无法及时发现个人防护设备 (PPE) 的滥用会导致许多伤害和经济损失。考虑到工业大厅的规模和工人数量,对可以帮助公司加强实施严格的工作场所安全标准的计算机化工具的需求不断增加。作为解决方案,我们提出了一个程序:1) 将 PPE 合规性问题减少到二元分类,2) 使可以安装在各种身体部位的任意类型和数量的 PPE 合规。为了证明这一假设,我们研究了 18 种不同的 PPE 类型,这些 PPE 用于各行各业,用于保护身体的 5 个生理部位/功能。HigherHRNet 姿态估计器用于定义感兴趣的 PPE 区域,同时评估了六种不同的图像分类架构,以评估所考虑区域的合规性/分类。所有分类器都在 ImageNet 数据集上进行了预训练,并使用本研究期间开发的专用数据集进行了微调。表现最好的模型是 MobileNetV2、Dense-Net 和 ResNet,而 MobileNetV2 被推荐为最佳选择,因为它的计算需求较低。与之前的研究相比,所提出的方法展示了具有独特能力的竞争性能,通过对 PPE 类型和相应身体部位的预定义列表进行轻微编辑,可以轻松采用以执行各种 PPE 的合规性。考虑到当前的数据/隐私/计算限制,建议该程序适用于以下 PPE 合规性的数字化:1) 自我检查点,

更新日期:2021-12-30
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