通过双空间增强提升风力机叶片分割精度
Dual-Space Augmented Intrinsic-LoRA for Wind Turbine Segmentation
- 结合图像与潜在空间的双重增强策略
- 在真实数据集上分割精度超越现有方法
- 适合风电巡检与自动化损伤检测场景
准确分割风力机叶片（WTB）图像对有效评估至关重要，直接影响自动化损伤检测系统的性能。尽管大型通用视觉模型取得进展，但在领域特定任务如WTB分割中仍表现不佳。为此，我们扩展了Intrinsic LoRA用于图像分割，并提出一种新颖的双空间增强策略，融合图像级和潜在空间增强。图像空间增强通过图像对之间的线性插值实现，潜在空间增强则通过引入基于噪声的潜在概率模型完成。该方法显著提升了分割精度，在WTB图像分割任务中超越当前最优方法。
原文摘要 · Abstract (English)
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.
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