mlsanityData to 14 Oct 2026

Dual-Space Augmented Intrinsic-LoRA for Wind Turbine Segmentation

arXiv 2412.20838 ↗First posted 30 Dec 2024cs.CV cs.AI cs.LGcited by 4

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Abstract (arXiv)

Accurate segmentation of wind turbine blade (WTB) images is critical for effective assessments, as it directly influences the performance of automated damage detection systems. Despite advancements in large universal vision models, these models often underperform in domain-specific tasks like WTB segmentation. To address this, we extend Intrinsic LoRA for image segmentation, and propose a novel dual-space augmentation strategy that integrates both image-level and latent-space augmentations. The image-space augmentation is achieved through linear interpolation between image pairs, while the latent-space augmentation is accomplished by introducing a noise-based latent probabilistic model. Our approach significantly boosts segmentation accuracy, surpassing current state-of-the-art methods in WTB image segmentation.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 20 of its 28 references that are in the map. First posted on arXiv 30 Dec 2024.

Builds on

20 of 28 references.

References · 20

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8 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Probabilistic Deep Discriminant Analysis for Wind Blade Segmentationcited by 1
  2. 2026Meteorological data and Sky Images meets Neural Models for Photovoltaic Power Forecastingcited by 0
  3. 2026Breaking the Sub-Millimeter Barrier: Eyeframe Acquisition from Color Imagescited by 0
  4. 2026Deep Image Segmentation via Discriminant Feature Learningcited by 0