Published Paper Details:

PPE stands for Personal Protective Equipment, which refers to protective gear or equipment designed to protect individuals from potential hazards or injuries in the workplace. PPE includes items such as helmets, gloves, safety glasses, vest. The use of PPE is essential in many industries, such as construction, manufacturing, and healthcare, to ensure the safety and well-being of workers. PPE must meet specific safety standards and be appropriate for the type of work being performed to provide effective protection against potential hazards. Deep learning techniques have enabled the automation of PPE detection, making the process more accurate and efficient. This paper proposes an extension to an existing PPE detection system using YOLOv5 that detects whether a person is wearing a helmet or not. The proposed system employs YOLOv8 and achieves an accuracy of 94.5% for detecting helmets, an improvement over the existing system's 92.2% accuracy. Additionally, the proposed system provides several new features, including the detection of vests, gloves, goggles, masks, and shoes. These new features enhance the system's safety capabilities by identifying workers who may be at risk due to a lack of appropriate PPE. The proposed system's performance demonstrates the potential of deep learning techniques in improving workplace safety, reducing workplace accidents, and promoting safer working environments.