摘要
Environmental pollution has had substantial impacts on human life,and trash is one of the main sources of such pollution in most countries.Trash classi-fication from a collection of trash images can limit the overloading of garbage dis-posal systems and efficiently promote recycling activities;thus,development of such a classification system is topical and urgent.This paper proposed an effective trash classification system that relies on a classification module embedded in a hard-ware setup to classify trash in real time.An image dataset isfirst augmented to enhance the images before classifying them as either inorganic or organic trash.The deep learning–based ResNet-50 model,an improved version of the ResNet model,is used to classify trash from the dataset of trash images.The experimental results,which are tested both on the dataset and in real time,show that ResNet-50 had an average accuracy of 96%,higher than that of related models.Moreover,integrating the classification module into a Raspberry Pi computer,which con-trolled the trash bin slide so that garbage fell into the appropriate bin for inorganic or organic waste,created a complete trash classification system.This proves the efficiency and high applicability of the proposed system.
