Automated Classification of Medical Waste Using Yolo V5 Model

Abstract: Managing biomedical waste safely is one of the toughest challenges faced by healthcare facilities, especially because manual segregation exposes workers to significant risks. To address this, our project introduces an automated medical waste sorting system designed to reduce human involvement and improve safety. At the heart of the system is a YOLO-based object detection model, which can identify commonly discarded medical items such as syringes, gloves, cotton pads, and masks using live camera input. Once an item is recognized, a Rasp berry Pi–powered robotic arm takes over, performing contactless pick-and-place operations to sort the waste into the correct bins. We tested the system under realistic operating conditions, and it consistently delivered accurate detection along with reliable robotic performance. These results demonstrate how combining deep learning with robotics can create a safer, more efficient approach to biomedical waste management, paving the way for smarter healthcare practices in the future.

Keywords: Medical Waste Management, Automated Waste Segregation, Object Detection, YOLO, Robotic Arm Automation, Raspberry Pi, Deep Learning