A Computer Vision-based System for Surgical Waste Detection

The world population is going through a difficult time due to the pandemic of COVID-19 while other disasters prevail. However, a new environmental catastrophe is coming because surgical masks and gloves are putting down anywhere, leading to the massive spreading of COVID-19 and environmental disasters. A significant number of masks and gloves are not properly managed. They are scattered around us such as roads, rivers, beaches, oceans and other places. Since these types of waste are turned into microplastics and chemicals are deadly harmful to the environment, human health and other species, especially for the aquatic animals on this planet. During the outbreaks of corona pandemic, surgical waste in the open place or seawater can create a fatal contagious environment. Putting them in a particular area can protect us from spreading infectious diseases. This study proposed a system that can detect surgical masks, gloves and infectious/biohazard symbols to put down infectious waste in a specific place or a container. Among the various types of surgical waste, this study prefers mask and gloves since it is currently the most widely used element due to the COVID-19. A novel dataset is created named MSG (Mask, Bio-hazard Symbol and Gloves), containing 1153 images and their corresponding annotations. Different versions of the You Only Look Once (YOLO) are applied as the architecture of this study; however, the YOLOX model outperforms.