Published Paper Details:

The utilization of Personal Protective Equipment (PPE) constitutes a crucial element in upholding worker safety amidst perilous environments within diverse industries. However, ensuring compliance with PPE usage can present a challenge due to the requisite manual inspection by supervisors or safety officers. Recent years have witnessed a burgeoning interest in constructing automated systems using computer vision and deep learning techniques to discern PPE usage. With this in view, the "Personal Protective Equipment Detection using YOLOv8" undertaking endeavors to forge a deep learning model that can automatically detect the presence of personal protective equipment such as masks, safety glasses, reflective vests, and hardhats. Through harnessing the YOLOv8 architecture and training the model on a dataset replete with images of individuals donning and eschewing PPE, the proposed system can accurately detect PPE usage. This can serve as a valuable instrument for organizations to ensure the safety of their employees and customers, as well as scrutinize compliance with safety regulations in public spaces. The project epitomizes the prodigious potential of deep learning in augmenting safety measures and curtailing risks throughout diverse industries.