用无监督区域生长与混合增强，高效准确分割风力机叶片。
Unsupervised Modular Adaptive Region Growing and RegionMix Classification for Wind Turbine Segmentation
- 通过无监督模块化自适应区域生长生成图像区域
- 在多个风电场数据上实现领先分割精度与泛化能力
- 适合缺乏标注数据的工业视觉检测场景
风力机可靠运行需频繁巡检，微小表面损伤即可能降低气动性能、减少发电量并加速叶片磨损。自动化巡检的核心是准确分割叶片图像。传统方法依赖大量像素级标注的深度学习模型，但数据标注成本高，难以扩展。本文提出一种注释高效的分割方法，将像素级任务重构为二分类区域任务。采用完全无监督、可解释的模块化自适应区域生长技术生成图像区域，结合图像自适应阈值与区域合并过程，将碎片化区域整合为连贯片段。为提升泛化性与分类鲁棒性，引入RegionMix增强策略，通过组合不同区域合成新训练样本。该框架在多个不同风电场数据上均实现当前最优分割精度与强跨站点泛化能力。
原文摘要 · Abstract (English)
Reliable operation of wind turbines requires frequent inspections, as even minor surface damages can degrade aerodynamic performance, reduce energy output, and accelerate blade wear. Central to automating these inspections is the accurate segmentation of turbine blades from visual data. This task is traditionally addressed through dense, pixel-wise deep learning models. However, such methods demand extensive annotated datasets, posing scalability challenges. In this work, we introduce an annotation-efficient segmentation approach that reframes the pixel-level task into a binary region classification problem. Image regions are generated using a fully unsupervised, interpretable Modular Adaptive Region Growing technique, guided by image-specific Adaptive Thresholding and enhanced by a Region Merging process that consolidates fragmented areas into coherent segments. To improve generalization and classification robustness, we introduce RegionMix, an augmentation strategy that synthesizes new training samples by combining distinct regions. Our framework demonstrates state-of-the-art segmentation accuracy and strong cross-site generalization by consistently segmenting turbine blades across distinct windfarms.
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