Sustainable Waste Management OOA-Enhanced MobileNetV2-TC Model for Trash Image Classification

Abstract

E-waste is an invisible, indirect waste that contaminates natural resources like the air, water, and soil, endangering the ecosystem, people, and animals. Long-term waste accumulation and contamination can harm the resources found in the environment. Since traditional waste management systems are very inefficient and the number of people living in urban areas is increasing, waste management systems in these areas face challenges. However, by combining a variety of sensors with deep learning (DL) models, waste resources can be used effectively. For this chapter, firstly, the Trashnet dataset with 2527 images in six classes and the VN-trash dataset, which comprises three classes and 5904 images, are collected. Then the collected images are preprocessed using truncated gaussian filter. After that, pre-trained convolutional neural network (CNN) models (Resnet20 and VGG19) are applied to the images in order to extract features. In order to enhance the predictive performance, this study then creates a MobileNetV2 model for trash classification (TC) called MNetV2-TC.