CCTV-Based Safety Gear Detection System for Enhancing Workplace Safety in Industrial Environments

Abstract

The implementation of CCTV surveillance for safety gear detection in industrial environments is becoming increasingly imperative to enhance workplace safety standards. This project aims to develop a robust system utilizing CCTV cameras to detect the presence or absence of essential safety gear worn by employees, such as helmets, goggles, and gloves. Through image processing techniques and machine learning algorithms, the system will analyze real-time video feeds to identify instances of non-compliance with safety regulations. The project will involve the design and development of algorithms for object detection and classification, as well as the integration of these algorithms into a user-friendly interface for industrial monitoring purposes. The anticipated outcome is a reliable CCTV surveillance system capable of proactively identifying safety gear violations, thereby mitigating potential hazards and promoting a safer work environment in industrial settings.

Index terms

CCTV surveillance, Safety gear detection, Industrial environments, Workplace safety standards, Image processing techniques, Machine learning algorithms, Object detection, Classification algorithms, Real-time video feeds, Non-compliance detection, Safety regulations, User-friendly interface, Industrial monitoring, Safety gear violations, Hazard mitigation, Proactive identification, Work environment safety, Robust system design.