Abstract
In the wake of the COVID-19 outbreak, there has been a dramatic uptick in the need for efficient medical waste management, making it imperative that more surgical waste management systems are developed. Used surgical masks and gloves are examples of potentially infectious materials that are the subject of this research. By utilizing its real-time object detection capabilities, the You Only Look Once (YOLO) deep learning-based object detection algorithm is used to identify surgical waste. Using the MSG dataset, a deep dive into the performance of three different YOLO architectures (YOLOv5, YOLOv7, and YOLOv8) was undertaken. According to the findings, YOLOv5-s, YOLOv7-x, and YOLOv8-m all perform exceptionally well when it comes to identifying surgical waste. YOLOv8-m was the best model, with a mAP of 82.4%, among these three. To mitigate post-COVID-19 infection risks and improve waste management efficiency, these results can be used to the creation of automated systems for medical waste sorting.
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