A Review of Deep Learning Approaches for Image-Based Waste Classification and Segregation

Abstract: The rapid increase in municipal solid waste has created significant environmental and public health challenges, making efficient waste segregation essential for recycling and sustainable waste management. Traditional manual classification methods are often labor-intensive, time-consuming, and prone to errors. This review examines recent advancements in image-based waste classification using deep learning techniques, particularly Convolutional Neural Network (CNN) architectures such as VGG16, VGG19, MobileNetV2, DenseNet121, EfficientNetB0, and Deep Convolutional Neural Networks (DCNNs). The reviewed studies demonstrate that deep learning models can accurately classify waste and improve segregation efficiency. Special focus is given to an improved multi-layered DCNN model that achieved 93.28% accuracy on a dataset of 25,077 waste images, outperforming several transfer learning models. The review also highlights commonly used datasets, evaluation metrics, current challenges, and future research directions, concluding that deep learning-based waste classification systems offer a promising solution for intelligent and sustainable waste management.

Keywords: Deep Learning, Waste Classification, Convolutional Neural Network (CNN), Smart Waste Management