Optimizing Waste Management Systems through Image Recognition and Feature Extraction Techniques Integrating DQN-Based Models

London College of Fashion, University of the Arts London, London, United Kingdom

^* Corresponding author: s.liang0620211@arts.ac.uk

Abstract

With increasing global attention on environmental protection and carbon neutrality goals, waste management has become a crucial component of achieving sustainability. Traditional waste disposal methods, such as manual sorting, identification, and crushing, often suffer from low efficiency and inconsistency. This study proposes a waste management system that integrates Artificial Intelligence (AI) to enhance the accuracy and efficiency of waste treatment through automation. The proposed system addresses waste classification, recognition, and fragmentation, utilizing advanced AI technologies like Graph Neural Networks (GNN), Convolutional Neural Networks (CNN), Visual Transformers (ViT), and Deep Reinforcement Learning (DRL). To optimize the waste management process, the study used the KTH-TIPS dataset, which contains 800 high-resolution images of various material types. Robust model training was ensured through a series of preprocessing and data augmentation techniques. The experimental results demonstrated that CNN and Transformer architectures, including MobileNet, ResNet, and ViT, achieved high accuracies of 100.00%, 100.00%, and 96.91%, respectively. While the GNN and VGG architectures had slightly lower accuracies of 82.88% and 84.57%, they still demonstrated competitive performance. The experimental results illustrate the variation in training and testing losses across different models over the training cycles, revealing the learning dynamics and efficiency of each model.