  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.
