A Literature Survey on Solid Waste Classification Using Deep Learning Techniques and Explainable AI

Abstract

The rapid growth of municipal solid waste has created significant environmental, economic, and public health challenges, highlighting the need for automated waste classification to support recycling, resource recovery, and smart waste management. This survey systematically reviews deep learning techniques for image-based solid waste classification, with particular emphasis on the role of Explainable Artificial Intelligence (XAI) in enhancing model transparency, interpretability, and trustworthiness. The review examines (i) publicly available waste image datasets and their characteristics and limitations, (ii) deep learning approaches, including convolutional neural networks (CNNs), transfer learning, vision transformers, and object-detection models, (iii) post-hoc XAI techniques, such as Grad-CAM, Grad-CAM++, SHAP, and LIME, and their integration into waste classification pipelines, and (iv) commonly adopted evaluation protocols and performance measures. The reviewed studies indicate that CNN-based and transfer-learning approaches can achieve high classification performance on benchmark datasets, although their effectiveness varies considerably with dataset composition, class distribution, experimental settings, and real-world conditions. XAI methods are increasingly employed to identify the visual features influencing model predictions and to assess whether automated decisions are based on semantically meaningful waste characteristics. The survey further identifies major challenges, including dataset bias and class imbalance, limited cross-dataset and real-world generalization, lack of standardized evaluation protocols, and insufficient interpretability. Finally, future research directions are discussed, including sustainability-aware deep learning, multimodal data integration, robust and generalizable XAI frameworks, and resource-efficient models for real-time waste classification and deployment in smart-city environments.