Medical waste sorting machine development with IoT and YOLO model utilization

Abstract The Internet of Things (IoT) application has been seen across various sectors to improve management practices. In medical waste management post-pandemic, the integration of IoT and artificial intelligence (AI) has notably enhanced sorting methods. The increased medical waste has brought about significant environmental challenges, impacting community health, land, and ocean ecosystems. We aim to develop a medical waste sorting system integrated with the You Only Look Once model (YOLO) and IoT for monitoring purposes. We can observe how effective the YOLO model is in sorting applications. This work trained and compared YOLO models from YOLO v5 to YOLO v9. YOLO v8 outperformed, and we implemented it into the sorting system. The mechanical, software, and programming elements were combined to develop the sorting prototype. From the training results, YOLO v8 achieved 98% mean average precision (mAP), 0.958 and 0.963 precision and recall, respectively. Other than that, the sorting evaluation was done during the final testing, with 93.75% accuracy. Further results were explained in the paper. The developed sorting prototype detected, classified, and sorted the medical waste, including facemasks, gloves, syringes, and sharp waste, based on the YOLO model. Further development is essential to improve the system in many aspects. Therefore, this system can be implemented practically in actual medical waste management. This work is a move that aligns with the third Sustainable Development Goal, where we focused on an automated medical waste system, ensuring sustainable health and Good Health and Well-being (SDG3).