Solid Waste Classification Using Deep Learning Techniques

Abstract

The management of waste is one of the challenging processes that involve different parameters, including environmental, climatic, technical, socio-economic parameters. Such complex problems are highly required to classify waste and optimize traditional methods. Recently, the advance of artificial intelligence (AI) and image processing have led to effective alternative computational approaches for addressing solid waste challenges. Although significant researches have been investigated in this domain, few works have used deep learning methods to solve diverse solid waste problems. This study proposes an intelligent model to categorize waste using convolutional neural networks. AlexNet, DenseNet121, and SqueezeNet have been implemented for performing the classification tasks. The obtained results showed great success in the classification process. DenseNet121 achieved the best performance with a value of 0.9415 in terms of accuracy.