Enhancing Laboratory Safety with AI: PPE Detection and Non-Compliant Activity Monitoring Using Object Detection and Pose Estimation

Ensuring workplace safety and adhering to regulatory standards in pharmaceutical manufacturing is vital. However, traditional manual monitoring methods are inefficient, prone to errors, and labor-intensive, resulting in potential safety risks and non-compliance penalties. This research introduces an automated deep learning framework that employs video analytics for real-time compliance monitoring, providing a scalable alternative to manual inspection processes. The system integrates YOLOv11n for detecting Personal Protective Equipment (PPE), such as gloves, masks, and goggles, identifying violations where PPE is either missing or improperly worn. Additionally, YOLOv8n-Pose is utilized to assess non-compliant postures, including actions like bending, hand-raising, and face-touching. A logging system tracks violations with precise timestamps, enabling efficient documentation for audits and regulatory purposes. A curated video dataset was developed and annotated using Roboflow, featuring both compliant and non-compliant actions. To enhance the model's robustness, preprocessing techniques such as resizing, contrast enhancement, and data augmentation were applied. The system’s performance, evaluated using metrics like mean Average Precision (mAP), F1- score, and precision, demonstrated an impressive 90% accuracy, with a mAP@50 of 92.1% and a processing speed of 25 frames per second (FPS), fulfilling the real-time monitoring criteria. This solution offers a scalable, real-time alternative to manual inspections, reducing human intervention, improving workplace safety, ensuring compliance with regulations, and automating the documentation process. Future developments aim to integrate IoT devices, employ edge computing, and incorporate cloud-based analytics to further enhance safety monitoring and compliance.