UrbanWaste: In-the-Bin Dataset for Waste Disposal Inspection with Multi-Granularity Hierarchical Labels

Abstract

Our world faces the challenge of efficiently and responsibly managing the ever-growing volume of urban waste. Many countries and regions have implemented categorized trash bins and require residents to sort their waste according to specified criteria. Proper waste classification by residents significantly reduces the workload in the waste disposal process. However, due to the lack of effective supervision during classification, the quality of waste sorting is often compromised. This misclassification can lead to higher pollution risks, lower recycling rates, and increased waste management costs and difficulties. To address this issue, we propose using images captured from within trash bins to supervise garbage delivery. We introduce UrbanWaste, an image dataset specifically designed for in-the-bin waste detection and segmentation. The dataset includes 25,254 RGB images and 140,008 annotated items, featuring dense annotations and multi-granularity labels across 193 distinct waste categories. We evaluated state-of-the-art segmentation models to understand their generalization and performance on UrbanWaste. Based on this dataset, we developed a comprehensive workflow for waste classification inspection, which has been deployed in real-world districts to assess the system's effectiveness. We hope UrbanWaste will inspire new directions in AI research for environmental sustainability.