The increase in non-biodegradable waste is a worldwide concern. Recycling facilities play a crucial role, but their automation is hindered by the complex characteristics of waste recycling lines like clutter or object deformation. In addition, the lack of publicly available labeled data for these environments makes developing robust perception systems challenging. Our work explores the benefits of multimodal perception for object segmentation in real waste management scenarios. First, we present SpectralWaste, the first dataset collected from an operational plastic waste sorting facility that provides synchronized hyperspectral and conventional RGB images. This dataset contains labels for several categories of objects that commonly appear in sorting plants and need to be detected and separated from the main trash flow for several reasons, such as security in the management line or reuse. Additionally, we propose a pipeline employing different object segmentation architectures and evaluate the alternatives on our dataset, conducting an extensive analysis for both multimodal and unimodal alternatives. Our evaluation pays special attention to efficiency and suitability for real-time processing and demonstrates how HSI can bring a boost to RGB-only perception in these realistic industrial settings without much computational overhead.

翻译：不可生物降解废物增加是一项全球性关注问题。回收设施发挥着关键作用，但其自动化进程受到废物回收线复杂特性（如杂乱或物体变形）的阻碍。此外，这些环境中缺乏公开可用的标记数据，使得开发稳健的感知系统面临挑战。本研究探索了多模态感知在真实废物管理场景中物体分割的优势。首先，我们提出SpectralWaste——首个从运营中的塑料废物分类设施采集的数据集，该数据集提供同步的高光谱与常规RGB图像。该数据集包含分类工厂中常见且需从主废物流中检测并分离的多类物体标签，这些物体因管理线安全或再利用等原因需要被识别。其次，我们提出一个采用不同物体分割架构的流水线，并在该数据集上评估各方案，对多模态与单模态替代方案进行了广泛分析。评估特别关注实时处理的效率与适用性，并证明高光谱成像（HSI）可在不带来显著计算开销的前提下，增强仅基于RGB的感知在实际工业场景中的性能。