Lightweight Deep Learning Algorithm for Sorting Medical Waste Embedded System

Abstract

The recent COVID-19 pandemic has led to an increased number of hospitalized patients, and in some countries, the overwhelming problem has even caused the collapse of the healthcare system. Thus, the associated waste generated and inadequate waste disposal may have deleterious impacts on public health and the surrounding environment. This paper aims to design and implement an automatic medical waste identification and sorting mechanism that can distinguish the items disposed of via a series of image processing procedures. Specifically, the system can classify, sort, and calculate the disposed items based on their visual appearance. To establish this, both software and hardware systems have been devised. Particularly, the vision system for object detection and classification is constructed based on several popular pre-trained convolutional neural networks. Upon verifying the type of waste (i.e., general infection, dangerous infection, and general garbage), an automatic sorting mechanism will be triggered to dispose of the object in the corresponding garbage bin. The experimentation has been validated on a total of self-collected 2025 images from 3 categories, and the best accuracy attained is 99.34% when adopting GoogLeNet as the backbone architecture.