| Popis: |
With the recent increase in the amount of disposed infectious waste due to COVID-19, a growing interest to develop an efficient, economical, and effective infectious waste segregation system has prompted both the health sector and the government. This study presented a microcontroller-based automated infectious waste segregation and disinfection system in a selected medical facility in Metro Manila, Philippines. The prototype system applying machine learning principles can identify three kinds of waste materials classified as electronic, pathological, and sharp wastes as interpreted by the YOLOv5 algorithm. In addition, an added feature of UV light mechanism to address the bacterial presence of Staphylococcus aureus and Escherichia coli was incorporated in the prototype to ensure disinfection. Results showed that the mean average precision (mAP) of identifying electronic, pathological, and sharp waste was 95.7, 79.9 and 94.5%, respectively. Moreover, it was found that there was a noticeable decrease in the bacterial count, signifying the effectiveness of the prototype and its promising potential for large-scale implementation. |
