Unsupervised Modular Adaptive Region Growing and RegionMix Classification for Wind Turbine Segmentation

2026-01-07

arXiv.org

AI Summary

This work addresses the challenge of scaling pixel-level segmentation of wind turbine blades for automated inspection, which typically relies on extensive manual annotations. To overcome this limitation, the authors propose a fully unsupervised and interpretable modular approach that reformulates the segmentation task as region generation followed by binary classification. The method leverages adaptive region growing—incorporating adaptive thresholding and region merging—and introduces RegionMix, a novel region-mixing augmentation strategy, to significantly enhance model generalization. Evaluated across multiple wind farm datasets, the proposed approach achieves state-of-the-art segmentation accuracy and demonstrates strong cross-site generalization capabilities without requiring any labeled data.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Unsupervised & Self-Supervised Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

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.

Problem

• wind turbine segmentation
• unsupervised segmentation
• annotation-efficient
• region classification
• cross-site generalization

Innovation

• Unsupervised Segmentation
• Modular Adaptive Region Growing
• RegionMix
• Annotation-Efficient Learning
• Wind Turbine Blade Segmentation