Few-Shot Visual Reasoning for Wind Turbine Blade Damage Detection via RAG with Vision-Language Model
Qianyu Zhou, Yang Zhang, Zhiling Chen, Fardin Jalil Piran, Farhad Imani, Jiong Tang · IFAC-PapersOnLine · 2025
Wind turbine blade inspection using drone-based imaging has emerged as a highly promising solution for scalable, low-cost monitoring of large wind farms. However, the majority of existing visual inspection methods still rely heavily on either handcrafted features or large-scale labeled datasets to train traditional deep learning models. These approaches face significant challenges in terms of adaptability, requiring time-consuming retraining or fine-tuning whenever new defect patterns, lighting conditions, or inspection angles arise. In this work, we propose a novel few-shot visual reasoning pipeline based on visual -text Retrieval-Augmented Generation (RAG) integrated with a pre-trained Vision-Language Model (VLM), designed to reduce reliance on manual labeling and enhance adaptability across blade inspection scenarios. Unlike conventional pipelines, our system does not require task-specific fine-tuning. Instead, it performs in-context few-shot reasoning by retrieving relevant visual-textual examples from a structured knowledge base and prompting the language model to reason over these alongside the current image’s description. To demonstrate its practical potential, we construct a domain-specific knowledge base including structured textual files and optional curated image-caption examples. We show that our visual-text RAG-VLM framework is able to reason about damage type and severity in a flexible, interpretable, and data-efficient manner.