Detection of Personal Protective Equipment (PPE) using an Anchor Free-Convolutional Neural Network

In industrial environments, the utilization of Personal Protective Equipment (PPE) is paramount for safeguarding workers from potential hazards. While various PPE detection methods have been explored in the literature, deep learning approaches have consistently demonstrated superior accuracy in comparison to other methodologies. However, addressing the pressing research challenge in deep learning-based PPE detection, which pertains to achieving high accuracy rates, non-destructive monitoring, and real-time capabilities, remains a critical need. To address this challenge, this study proposes a deep learning model based on the Yolov8 architecture. This model is specifically designed to meet the rigorous demands of PPE detection, ensuring accurate results. The methodology involves the creation of a custom dataset and encompasses rigorous training, validation, and testing processes. Experimental results and performance evaluations validate the proposed method, illustrating its ability to achieve highly accurate results consistently. This research contributes to the field by offering an effective and robust solution for PPE detection in industrial environments, emphasizing the paramount importance of accuracy, non-destructiveness, and real-time capabilities in ensuring workplace safety.