  Industry 4.0 introduced AI as a transformative solution for modernizing
manufacturing processes. Its successor, Industry 5.0, envisions humans as
collaborators and experts guiding these AI-driven manufacturing solutions.
Developing these techniques necessitates algorithms capable of safe, real-time
identification of human positions in a scene, particularly their hands, during
collaborative assembly. Although substantial efforts have curated datasets for
hand segmentation, most focus on residential or commercial domains. Existing
datasets targeting industrial settings predominantly rely on synthetic data,
which we demonstrate does not effectively transfer to real-world operations.
Moreover, these datasets lack uncertainty estimations critical for safe
collaboration. Addressing these gaps, we present HAGS: Hand and Glove
Segmentation Dataset. This dataset provides 1200 challenging examples to build
applications toward hand and glove segmentation in industrial human-robot
collaboration scenarios as well as assess out-of-distribution images,
constructed via green screen augmentations, to determine ML-classifier
robustness. We study state-of-the-art, real-time segmentation models to
evaluate existing methods. Our dataset and baselines are publicly available:
https://dataverse.tdl.org/dataset.xhtml?persistentId=doi:10.18738/T8/85R7KQ and
https://github.com/UTNuclearRoboticsPublic/assembly_glovebox_dataset.
