Sustainable Waste Management Through Deep Learning: A Knowledge Distillation Framework for Real-Time Garbage Classification

Effective waste sorting is central to circular economy goals and sustainable waste management: it maximizes recycling yields, diverts waste from landfills, and reduces the environmental burden of solid waste disposal. Accurate automated sorting using deep learning can achieve this at scale, yet high-performing classifiers are too computationally demanding for the low-cost embedded hardware used in sorting facilities. We propose the KD-Garbage Framework, which applies knowledge distillation to transfer predictive knowledge from a high-capacity teacher model to a lightweight student model, enabling deployment-ready classifiers that approach or exceed teacher-level accuracy without any added inference cost. We also introduce a 15,681-image garbage dataset organized into 13 classes defined by recycling and disposal pathway, assembled from 12 public sources and original photography, with all labels manually verified. Two teacher models were paired with 16 lightweight convolutional neural network (CNN) student architectures and benchmarked on a Raspberry Pi 5 at a minimum throughput of five frames per second. Knowledge distillation reduced misclassification rates by 10–25% across all student architectures. The best-performing student, RegNetY-1.6GF, achieved a balanced accuracy of 0.9129, surpassing both teacher models while sustaining real-time throughput on the target hardware, demonstrating a practical pathway toward scalable, AI-enabled sustainable waste management.