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Related Concept Videos

Personal Protective Equipment01:20

Personal Protective Equipment

Personal protective equipment (PPE) is unique clothing or equipment worn by an employee to minimize or prevent exposure to infectious agents. PPE creates a barrier between the employee and the infectious materials. PPE must be readily available in the patient care area. PPE includes gloves, gowns and aprons, masks and respirators, goggles, face shields, shoes, and headcovers:
Three-Winding Transformers01:19

Three-Winding Transformers

Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
In the per-unit equivalent circuit of a grounded Y-Y three-phase...
Instrument Transformers01:23

Instrument Transformers

Instrument transformers, comprising voltage transformers (VTs) and current transformers (CTs), play crucial roles in power substations by providing isolated replicas of current or voltage for measurement and protection purposes. Voltage transformers reduce the primary voltage to levels suitable for relay operation and measurement, while current transformers scale down the primary current. The primary winding of a current transformer often consists of a single turn, achieved by threading the...

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Related Experiment Videos

On Vision Transformer Explainability for Personal Protective Equipment Detection: A Qualitative and Quantitative

Miriam Di Renzo1, Filomena Niro1, Patrizia Agnello2

  • 1Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, 86100 Campobasso, Italy.

Journal of Imaging
|May 26, 2026
PubMed
Summary

Federated Machine Learning (FML) enhances worker safety by monitoring Personal Protective Equipment (PPE) usage with Deep Learning (DL). This study validates FML explainability methods for robustly identifying critical safety areas in industrial images.

Keywords:
CLS heatmapGrad-CAMPPEattention heatmapexplainabilityfederated machine learningprotective equipmentrollout heatmapsafety

Related Experiment Videos

Area of Science:

  • Industrial Safety
  • Artificial Intelligence
  • Machine Learning

Background:

  • Worker safety in industrial settings relies on correct Personal Protective Equipment (PPE) usage.
  • Deep Learning (DL) offers potential for monitoring PPE compliance.
  • Federated Machine Learning (FML) enables privacy-preserving and explainable AI for sensitive data.

Purpose of the Study:

  • To evaluate the robustness and consistency of explainability algorithms in identifying crucial areas for PPE classification.
  • To assess the effectiveness of similarity indices in validating explainability models within an FML framework.
  • To analyze the performance of explainability techniques in real-world industrial safety monitoring.

Main Methods:

  • Utilized a dataset of 1600 real-world images of workers with and without various PPE (helmets, reflective vests).
  • Employed Federated Machine Learning (FML) for privacy-preserving analysis.
  • Applied similarity indices such as Structural Similarity Index Measure (SSIM), Visual Information Fidelity (VIF), and Similarity-based Consistency Check (SCC) to evaluate explainability.

Main Results:

  • High mean similarity index values were observed, indicating robust explainability.
  • Intra-client study yielded mean values of 0.99 (SSIM), 0.96 (VIF), and 0.96 (SCC).
  • Inter-client analysis showed mean values of 0.96 (SSIM), 0.91 (VIF), and 0.71 (SCC), demonstrating consistent performance across different data distributions.

Conclusions:

  • The study confirms the robustness and consistency of explainability algorithms for PPE classification using FML.
  • The employed similarity indices effectively validate the reliability of AI models in identifying critical safety elements.
  • FML-powered explainable AI shows significant promise for enhancing industrial safety monitoring and compliance.