Lightweight Intelligent Detection System for Personal Protective Equipment in Laboratories of Higher Education Institutions

Ensuring laboratory safety, particularly in high-risk environments like chemical and biological labs, is crucial in preventing accidents and protecting personnel. Personal Protective Equipment (PPE) plays a critical role in safeguarding workers from hazardous materials, machinery, and environmental risks. However, manually monitoring PPE usage in large laboratories or educational institutions can be inefficient, time-consuming, and prone to errors. This project presents an intelligent system for automated PPE detection using advanced object detection models like YOLOv5, YOLOv8, and YOLOv11. The proposed system uses deep learning techniques to detect whether laboratory personnel are wearing essential PPE such as gloves, goggles, lab coats, and masks.

By using deep learning models that specialize in object detection, the system processes images of laboratory environments and identifies PPE compliance with high accuracy. The system can also alert administrators when PPE compliance issues are detected, enabling corrective action. Additionally, the system is designed to be lightweight, ensuring it can operate on resource-constrained devices without the need for high-performance GPUs. The use of YOLO models ensures efficient performance, making it ideal for deployment in various laboratory settings. The system is built with flexibility in mind and can be integrated with existing infrastructure to enhance laboratory safety management.

This automated system provides several advantages over traditional manual inspection methods, including increased accuracy, faster monitoring, and reduced human involvement. With its ability to be deployed on low-resource devices, it opens up possibilities for widespread use across different laboratory environments. Ultimately, this intelligent system has the potential to improve laboratory safety practices, making them more reliable and less dependent on human oversight.

Keywords: PPE detection, YOLOv5, YOLOv8, YOLOv11, object detection, deep learning, laboratory safety, automated inspection, intelligent system, automated compliance.