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SteelHuman: Predicting Operator Estimates of Steel-Scrap Composition from Sparse RGB Views — arXiv:2611.04317

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.04317
License (as archived): CC0-1.0
Archived: 2026-11-04T12:56:17.653Z
SHA-256 of the archived text: 3df810cdbdcf8515358c54dd5a3f96f830942542df859cfda288bb0f1db16ce7
Stable URL of this page: https://metraipolis.com/documento/dc_70eebc6cbff1

How to cite

SteelHuman: Predicting Operator Estimates of Steel-Scrap Composition from Sparse RGB Views — arXiv:2611.04317 — arXiv (en), https://arxiv.org/abs/2611.04317 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_70eebc6cbff1 (Metraipolis archive dc_70eebc6cbff1, sha256 3df810cdbdcf8515358c54dd5a3f96f830942542df859cfda288bb0f1db16ce7)

BibTeX

@misc{dc70eebc6cbff1,
  title = {SteelHuman: Predicting Operator Estimates of Steel-Scrap Composition from Sparse RGB Views — arXiv:2611.04317},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.04317},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_70eebc6cbff1; sha256 3df810cdbdcf8515358c54dd5a3f96f830942542df859cfda288bb0f1db16ce7},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.04317. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Steel scrap composition estimation is an important part of yield planning and fair supplier compensation. Measuring the composition of a multi-ton pile is an inherently laborious process, so the assessment is often left to trained human operators. Recent works tackle composition-level tasks, but require structured acquisition and multi-stage preprocessing. We study a simpler approach to steel scrap load assessment by aggregating features from sparse, handheld views of the pile. On 600 out-of-fold piles, frozen DINOv2 tokens aggregated uniformly across views reach 13.4% total variation (TV) distance against operator estimates. Most remaining error lies in allocation among four thickness-defined heavy melting scrap (HMS) classes, yet image-dependent allocation reduces within-HMS TV from 25.1% under a training-set split prior to 11.2%. Due to the lack of public datasets and benchmarks for predicting composition, external evaluation relies on the public DOES dataset intended for classification. A classifier using the same frozen features reaches 93.97% accuracy, exceeding the published results we compare against. The composition model also aligns with related DOES categories without adaptation.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.CV, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2611.04317 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-04T12:33:47.128Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).