Domestic Trash Classification with Transfer Learning Using VGG16
Haruna Abdu, Mohd Halim Mohd Noor
Abstract
Environmental contamination is a major issue affecting all inhabitants living in any environment. The domestic environment is engulfed with many trash items such as solid and toxic trashes, leading to severe environmental contamination and causing life-threatening diseases if not appropriately managed. Trash classification is at the heart of these issues because the inability to classify the trash leads to difficulty in recycling. Humans categorize trash based on what they understand about the trash object rather than on the recyclability status of an object, which frequently leads to incorrect classification in manual classification. Additionally, coming into contact with toxic waste directly could be physically dangerous for those involved. Few machine learning and Deep Learning (DL) techniques were proposed using benchmarked trash classification datasets. However, most benchmarked datasets used to train DL models have a transparent or white background, which leads to a lack of model generalization, particularly in the real world. In this paper, we propose a Deep Learning model based on the VGG16 Architecture that can accurately classify various types of trash objects. On the TrashNet dataset plus the images collected in the wild, we achieved an accuracy of more than 96%.