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

Sara Inácio; Hugo Proença; João C. Neves

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arXiv · 2601.02299

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

Sara Inácio·Hugo Proença·João C. Neves

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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.

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BibTeX RIS

Sara Inácio, Hugo Proença, João C. Neves. 2026-01-07. SortWaste: A Densely Annotated Dataset for Object Detection in Industrial Waste Sorting. https://arxiv.org/abs/2601.02299

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