Classification of recyclable waste using deep learning architectures

Year 2022, Volume: 1 Issue: 3, 122 - 128, 28.10.2022

Arzu Sevinç Fatih Özyurt

https://doi.org/10.5505/fujece.2022.83997

Abstract

Managing waste in big cities is a big problem. Wastes are dangerous in terms of causing environmental pollution and affecting human
health. In particular, solid wastes such as glass and plastic do not dissolve in the soil for a long time and pollute the environment. By
recycling such solid wastes, the surrounding waste can be reduced. Therefore, it is important to classify waste and to recycle the
separated waste. In this study, a data set consisting of 22500 waste images was used. The data set contains color image data with a size
of 227 x 227 pixels. The data used in the study are divided into two as organic and recyclable waste. This study proposes a deep
learning-based system for classifying waste. With such a system, wastes can be classified and recycled. The data was trained with the
ResNet 50 architecture and the CNN architecture created to classify waste, and accuracy rates were compared. The CNN architecture
created to classify waste is more successful for this data set with an accuracy rate of 91.84%.

Keywords

Waste classification, Deep learning, Convolutional neural network, ResNet-50 architecture

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