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Benchmarking Lightweight YOLO Object Detectors for Real-Time Hygiene Compliance Monitoring.

Leen Alashrafi1, Raghad Badawood1, Hana Almagrabi1

  • 1Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

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Summary
This summary is machine-generated.

This study benchmarks lightweight object detection models for automated personal protective equipment (PPE) compliance monitoring. YOLOv10n demonstrated the best balance of accuracy and efficiency for real-time hygiene enforcement in regulated environments.

Keywords:
IoT-integrated systemsbenchmarkingcomputer visiondeep learninghygiene compliancemodel efficiencyobject detectionpersonal protective equipment (PPE)real-time monitoring

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Public Health

Background:

  • Hygiene compliance in regulated environments (food processing, hospitals) necessitates reliable personal protective equipment (PPE) detection.
  • Manual inspection for PPE usage (gloves, masks, hairnets) is inefficient for continuous, real-time monitoring.

Purpose of the Study:

  • To benchmark lightweight object detection models (YOLOv8n, YOLOv10n, YOLOv12n) for automated PPE compliance monitoring.
  • To evaluate model performance using both accuracy and efficiency metrics for practical deployment.

Main Methods:

  • Utilized a curated dataset of over 31,000 annotated images covering seven PPE compliance classes.
  • Evaluated three nano-scale YOLO models on detection accuracy (mAP, precision, recall) and efficiency (inference speed, model size, GFLOPs).

Main Results:

  • YOLOv10n achieved the highest mAP@50 (85.7%) with competitive efficiency, suitable for IoT deployments.
  • YOLOv8n offered superior localization accuracy at stricter thresholds (mAP@50-95).
  • YOLOv12n prioritized ultra-lightweight operation, sacrificing some accuracy.

Conclusions:

  • YOLOv10n is a strong candidate for real-time PPE compliance systems due to its balanced performance.
  • The study provides a reproducible, deployment-aware framework for computer vision in hygiene-critical settings.
  • Results guide the selection of nano-scale detection models for enhanced hygiene monitoring.