构建工业垃圾分拣密集标注数据集，提升自动化分拣准确率
SortWaste: A Densely Annotated Dataset for Object Detection in Industrial Waste Sorting
- 采集真实垃圾处理厂数据，实现细粒度目标检测标注
- 提出ClutterScore量化场景复杂度，塑料检测mAP达59.7%
- 为算法评估提供新基准，适合研究工业视觉与机器人分拣者
人口增长导致废弃物产量上升，传统人工分拣效率低且存在健康风险。现有自动化方案因真实垃圾流中物体多样、杂乱和视觉复杂而表现不佳，主要受限于缺乏真实世界数据集。为此，我们构建了SortWaste——一个来自物料回收设施的密集标注目标检测数据集。同时提出ClutterScore，一种基于物体数量、类别与尺寸熵、空间重叠等代理指标的客观场景复杂度度量方法。我们还对当前主流目标检测模型进行了全面评测，分析其在不同复杂度下的表现。尽管在仅检测塑料的任务中达到59.7%的mAP，但高杂乱场景下性能显著下降，凸显该领域亟需更复杂、更具挑战性的数据集。
原文摘要 · Abstract (English)
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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