Deep learning implementation using convolutional neural network in inorganic packaging waste sorting

Abstract

Municipal solid waste is a significant issue that causes environmental contamination. One of the most prevalent wastes that is difficult to decompose is waste from inorganic packaging. Inorganic packaging waste management can be done by sorting waste as the first step before going through subsequent processing. However, waste sorting is currently still difficult to do by human power in waste management facilities, so it is necessary to design a system that can assist the waste sorting process. This research aims to develop a model that can classify inorganic packaging at waste processing sites. To develop the model, we used five pre-trained Convolutional Neural Network (CNN) architectures, namely Xception, Inception V3, ResNet-50, Resnet-50 V2, and DenseNet-201. Then, the best architecture based on some metric performances will be tuned. The result displayed that the CNN model with Densenet 201 architecture, accompanied by tuning, achieved the best performance to classify the waste. The accuracy for the validation dataset is 95.31 %, the accuracy for the testing dataset is 95.6 %, precision is 0.96, recall is 0.96, and the F1-score is 0.96. The results of those performance metrics show that the model can predict the image of inorganic packaging waste well for further application to an automated waste sorting system.