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.

翻译：随着人口增长推动废物产量不断增加，有效管理和回收材料面临严峻挑战。人工分拣是常见做法，但其处理大规模废物流的效率低下，且对工作人员存在健康风险。另一方面，现有自动化分拣方法在处理真实废物流的高可变性、混杂性和视觉复杂性方面仍存在困难。缺乏真实场景的废物分拣数据集是该领域自动化系统发展不足的主要原因。为此，我们推出SortWaste——一个从物料回收设施采集的密集标注目标检测数据集。此外，我们通过提出ClutterScore来推动分拣线废物检测的标准化，该客观度量通过一组影响视觉复杂性的代理指标（如物体数量、类别与尺寸熵值、空间重叠度）来评估场景难度级别。除上述贡献外，我们还建立了当前最先进目标检测模型的广泛基准测试，依据所提度量评估的难度级别详细呈现其性能表现。尽管在纯塑料检测任务中取得了有希望的结果（mAP达59.7%），但在高度混杂场景中性能显著下降。这凸显了该领域需要更具挑战性的新型数据集。