Generic compliance of industrial PPE by using deep learning techniques. (April 2022)

• Record Type: Journal Article
• Authors: Vukicevic, Arso M.; Djapan, Marko; Isailovic, Velibor; Milasinovic, Danko; Savkovic, Marija; Milosevic, Pavle
• Is Part Of: Safety science. Volume 148(2022)
• Publication Date: 2022-04
• Subjects: PPE -- Compliance -- Artificial intelligence -- Industrial engineering -- Workplace safety; Industrial accidents -- Periodicals; Accident Prevention -- Periodicals; Safety -- Periodicals; Travail -- Accidents -- Périodiques; 363.11
• DOI: 10.1016/j.ssci.2021.105646
• Languages: English
• ISSNs: 0925-7535

Abstract

Graphical abstract: Highlights: Automated PPE compliance could reduce the number and costs of workplace injuries. The HigherHRNet pose estimator was used for defining the regions of interest (ROI). The problem of PPE compliance is reduced to the binary classification of ROIs. Study considered 18 PPEs used for protecting 5 physiological body parts/functions. MobileNetV2 was recommended as the most optimal classifier for PPE compliance. Abstract: 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.