Classification of Recyclable Materials Using Efficient Deep Learning Models and Benchmarking of GPU Performance

Abstract

One of the consequences of climate change and global warming results from excessive consumption of sources. To slow down global warming and increase energy saving, recycling, within the framework of waste management, needs to be widely implemented. Waste management and recycling is not only environmentally advantageous but also of great importance for a sustainable economy. Preferring smart systems instead of human workers is a socially important step that allows people to work in more welfare environments. Intelligent waste management approaches open up a major research area.

The main objective of the study is aimed to contribute to the efficient collection of recyclable materials from the end consumer, to optimize the collection process and to reduce the workload in the waste institution. In addition to the TrashNet dataset used in the previous classification of recyclable materials, an expanded dataset is collected, and a more advanced version is obtained. Data from three different classes, including glass, plastic, and metal waste, were collected and the current dataset was enhancement from 2527 to 6136. The new extended dataset is called TrashX. Therefore, not only the methods used in the literature have been improved, but also the convolutional neural network-based models used are tested. All results are evaluated according to performance criteria. In this research, 6 different recyclable waste classifications are made on a progressed dataset consisting of 6136 RGB images. Within the scope of this study, the largest dataset in the literature was created. For this purpose, high performance and robust models such as MobileNet, RecycleNet, and EfficientNet are offered. One of the most important factors of the study is that the performance of the models is evaluated in terms of time on different hardware. This benchmarking light on researchers to improve intelligence recycling and waste management systems.

Finally, the experiments are run to compare the performances of the methods for both TrashNet and the TrashX datasets. The experimental results demonstrate that EfficientNet-b3 efficiency 93.8% and 97.3% in terms of accuracy for Trashnet and TrashX datasets separately and thus it outperforms the many recent approaches for trash classification on both experimental datasets.