SortWaste: A Densely Annotated Dataset for Object Detection in Industrial Waste Sorting

2026-01-05

arXiv.org

AI Summary

This study addresses the limitations of existing automated sorting systems for industrial waste, which struggle with the high variability, clutter, and visual complexity of real-world waste streams, compounded by a lack of high-quality annotated data from authentic scenarios. To bridge this gap, the authors introduce SortWaste, the first densely annotated object detection dataset specifically designed for industrial waste sorting, along with ClutterScore—a novel metric that quantifies scene-level visual complexity using proxy variables such as object count, category and size entropy, and spatial overlap. Systematic evaluation of state-of-the-art detection models across varying complexity levels reveals significant performance degradation in highly cluttered scenes (e.g., mAP of 59.7% for plastics), highlighting current methodological limitations and establishing a standardized benchmark and toolkit for future research in waste detection.

Technology Category

• Computer Vision: Object Detection & Categorization
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Constraint Satisfaction and Optimization: Other Foundations of Constraint Satisfaction

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

The increasing production of waste, driven by population growth, has created challenges in managing and recycling materials effectively. Manual waste sorting is a common practice; however, it remains inefficient for handling large-scale waste streams and presents health risks for workers. On the other hand, existing automated sorting approaches still struggle with the high variability, clutter, and visual complexity of real-world waste streams. The lack of real-world datasets for waste sorting is a major reason automated systems for this problem are underdeveloped. Accordingly, we introduce SortWaste, a densely annotated object detection dataset collected from a Material Recovery Facility. Additionally, we contribute to standardizing waste detection in sorting lines by proposing ClutterScore, an objective metric that gauges the scene's hardness level using a set of proxies that affect visual complexity (e.g., object count, class and size entropy, and spatial overlap). In addition to these contributions, we provide an extensive benchmark of state-of-the-art object detection models, detailing their results with respect to the hardness level assessed by the proposed metric. Despite achieving promising results (mAP of 59.7% in the plastic-only detection task), performance significantly decreases in highly cluttered scenes. This highlights the need for novel and more challenging datasets on the topic.

Problem

• waste sorting
• object detection
• dataset
• visual complexity
• clutter

Innovation

• SortWaste
• object detection
• ClutterScore
• waste sorting
• dense annotation