Computerized compliance of Personal Protective Equipment (PPE) is an emerging topic in academic literature that aims to enhance workplace safety through the automation of compliance and prevention of PPE misuse (which currently relies on manual employee supervision and reporting). Although trends in the scientific literature indicate a high potential for solving the compliance problem by employing computer vision (CV) techniques, the practice has revealed a series of barriers that limit their wider applications. This article aims to contribute to the advancement of CV-based PPE compliance by providing a comparative review of high-level approaches, algorithms, datasets, and technologies used in the literature. The systematic review highlights industry-specific challenges, environmental variations, and computational costs related to the real-time management of PPE compliance. The issues of employee identification and identity management are also discussed, along with ethical and cybersecurity concerns. Through the concept of CV-based PPE Compliance 4.0, which encapsulates PPE, human, and company spatio-temporal variabilities, this study provides guidelines for future research directions for addressing the identified barriers. The further advancements and adoption of CV-based solutions for PPE compliance will require simultaneously addressing human identification, pose estimation, object recognition and tracking, necessitating the development of corresponding public datasets.

Despite its two-dimensional nature, X-ray angiography (XRA) has served as the gold standard imaging technique in the interventional cardiology for over five decades. Accordingly, demands for tools that could increase efficiency of the XRA procedure for the quantitative analysis of coronary arteries (CA) are constantly increasing. The aim of this study was to propose a novel procedure for three-dimensional modeling of CA from uncalibrated XRA projections. A comprehensive mathematical model of the image formation was developed and used with a robust genetic algorithm optimizer to determine the calibration parameters across XRA views. The frames correspondences between XRA acquisitions were found using a partial-matching approach. Using the same matching method, an efficient procedure for vessel centerline reconstruction was developed. Finally, the problem of meshing complex CA trees was simplified to independent reconstruction and meshing of connected branches using the proposed nonuniform rational B-spline (NURBS)-based method. Because it enables structured quadrilateral and hexahedral meshing, our method is suitable for the subsequent computational modelling of CA physiology (i.e. coronary blood flow, fractional flow reverse, virtual stenting and plaque progression). Extensive validations using digital, physical, and clinical datasets showed competitive performances and potential for further application on a wider scale.

The compliance of industrial personal protective equipment (PPE) still represents a challenging problem considering size of industrial halls and number of employees that operate within them. Since there is a high variability of PPE types/designs that could be used for protecting various body parts and physiological functions, this study was focused on assessing the use of computer vision algorithms to automate the compliance of head-mounted PPE. As a solution, we propose a pipeline that couples the head ROI estimation with the PPE detection. Compared to alternative approaches, it excludes false positive cases while it largely speeds up data collection and labeling. A comprehensive dataset was created by merging public datasets PictorPPE and Roboflow with author’s collected images, containing twelve different types of PPE was used for the development and assessment of three deep learning architectures (Faster R-CNN, MobileNetV2-SSD and YOLOv5)—which in literature were studied only separately. The obtained results indicated that various deep learning architectures reached different performances for the compliance of various PPE types—while the YOLOv5 slightly outperformed considered alternatives (precision 0.920 ± 0.147, and recall 0.611 ± 0.287). It is concluded that further studies on the topic should invest more effort into assessing various deep learning architectures in order to objectively find the optimal ones for the compliance of a particular PPE type. Considering the present technological and data privacy barriers, the proposed solution may be applicable for the PPE compliance at certain checkpoints where employees can confirm their identity.