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Robust Online Aero-Engine Blade Defect Detection via Dual-Alignment Test-Time Adaptation — arXiv:2610.00067

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.00067
License (as archived): CC0-1.0
Archived: 2026-10-06T19:10:41.916Z
SHA-256 of the archived text: b5a13a8e8c0e690f4280d4c73354fdacbdaa4b04cbea3f6e4c489d0e61c0938b
Stable URL of this page: https://metraipolis.com/documento/dc_197332104b05

How to cite

Robust Online Aero-Engine Blade Defect Detection via Dual-Alignment Test-Time Adaptation — arXiv:2610.00067 — arXiv (en), https://arxiv.org/abs/2610.00067 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_197332104b05 (Metraipolis archive dc_197332104b05, sha256 b5a13a8e8c0e690f4280d4c73354fdacbdaa4b04cbea3f6e4c489d0e61c0938b)

BibTeX

@misc{dc197332104b05,
  title = {Robust Online Aero-Engine Blade Defect Detection via Dual-Alignment Test-Time Adaptation — arXiv:2610.00067},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.00067},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_197332104b05; sha256 b5a13a8e8c0e690f4280d4c73354fdacbdaa4b04cbea3f6e4c489d0e61c0938b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.00067. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Reliable visual inspection is essential for quality assurance in aero-engine blade manufacturing, where defect appearance may vary across production lines, imaging conditions, blade poses, and surface backgrounds. Such domain shifts cause a mismatch between training and deployment data and degrade the reliability of deep defect detectors in online inspection. This problem is particularly challenging because aero-engine blade images usually contain sparse defects, making pseudolabel-based adaptation vulnerable to noisy or missing predictions. To address this issue, we propose Aero-engine Blade Defect Detector (ABDD), an online adaptive detection framework based on test-time adaptation. ABDD introduces a Dual-Alignment Strategy to jointly adapt global visual style and local defect morphology by combining feature-statistics alignment with pseudo-box alignment. To reduce error accumulation from unreliable pseudo labels, an Uncertainty-aware Box Filtering mechanism evaluates pseudo boxes using classification confidence, classification entropy, and localization entropy. In addition, a lightweight Sparse Dilated Mona module enables parameter-efficient delta tuning while limiting source-domain forgetting. ABDD is evaluated on CD-AeBD and HD-AeBD under multiple domain-shift scenarios, with TTA strategies compared under a unified RT-DETR + Swin-T architecture. Experiments show that ABDD consistently improves detection robustness under domain shifts, and its practicality is further validated on an industrial inspection platform.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-10-02 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2610.00067 — 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 2026-10-06T17:37:36.649Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-10-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).