2024-DTU Risø Wind Turbine Blade Inpsction Video Dataset Description

AI-based blade identification in operational wind turbines through similarity analysis aided drone inspection Shohreh Sheiati a, *, Xiaodong Jia a, Malcolm McGugan a, Kim Branner a, Xiao Chen a, * a Technical University of Denmark, Department of Wind and Energy Systems, Roskilde 4000, Denmark. Abstract Developing an automated method to monitor the structural health of each individual wind turbine blade over its lifespan is imperative for timely warning of potential damages. This study proposes an AI-based image recognition model to identify individual blades based on their unique surface features. To eliminate the influence of the image background on the identification task, a deep learning segmentation method was used to isolate the blade image from the complex background in optical images captured by drone. Subsequently, we utilized and repurposed similarity-based Siamese networks to establish a blade search capability to identify images of the same blade. This model automatically retrieves the corresponding blade images in response to a single query image. The results show that our trained model distinguishes similar images from non-similar images and identifies the blades from unseen images successfully. Our developed similarity-based identification method ensures continuous tracking and monitoring of the structural health of each individual blade over time. Dataset description We captured 29 videos from a wind turbine situated at DTU Risø campus, Roskilde municipality, Denmark. These videos were recorded under diverse circumstances, accounting for varying imaging conditions and different segments of the blades, using a DJI Mavic 2 drone. We organized the dataset into a folder named “Dataset”, which comprises 29 subfolders, each dedicated to one of the original videos along with extracted image frames and their corresponding masks. The initial set of 24 videos in the collection was used for training and validation processes. The remaining 5 videos, not utilized during the training process, were used for testing. Masks were obtained from two techniques: bi-level thresholding and the GrabCut foreground-background segmentation method. The bi-level thresholding technique was applied to images with clear contrast between the foreground (blade) and background (all videos except 6, 13, 15, 25). For images with very complex backgrounds containing divers objects with different colours and textures (videos: 6, 13, 15, 25), the GrabCut method was employed. Contact: [e-mail]

面向运行中风力涡轮机的AI叶片识别：基于相似性分析的无人机辅助巡检方法
Shohreh Sheiati a、*、Xiaodong Jia a、Malcolm McGugan a、Kim Branner a、Xiao Chen a、*
丹麦技术大学（Technical University of Denmark）风能与能源系统系，罗斯基勒4000，丹麦。

**摘要**：在风力涡轮机叶片全生命周期内实现单叶片结构健康状态的自动化监测，对及时预警潜在损伤至关重要。本研究提出一种基于人工智能的图像识别模型，可依据叶片独特的表面特征实现单叶片识别。为消除图像背景对识别任务的干扰，本研究采用深度学习分割方法（deep learning segmentation method），从无人机采集的光学图像中将叶片图像从复杂背景中分离出来。随后，我们利用并改造了基于相似性的孪生神经网络（Siamese Network），以构建叶片检索能力，实现同叶片图像的精准识别。该模型可针对单张查询图像自动检索得到对应的叶片图像。实验结果表明，训练完成的模型能够有效区分相似与非相似图像，并成功从未见过的图像中识别出目标叶片。本研究所开发的基于相似性的识别方法，可实现单叶片结构健康状态的长期连续跟踪与监测。

**数据集说明**：本研究从丹麦罗斯基勒市丹麦技术大学瑞斯奥校区（DTU Risø Campus）的一台风力涡轮机上采集了29段视频。所有视频均由大疆御2（DJI Mavic 2）无人机拍摄，拍摄场景涵盖多种成像条件与叶片不同区段。我们将数据集整理至名为"Dataset"的文件夹中，该文件夹包含29个子文件夹，每个子文件夹对应一段原始视频，内含提取的图像帧及其对应的掩码图像。本数据集的前24段视频用于模型训练与验证流程，剩余5段未参与训练的视频则用于测试环节。掩码图像通过两种方法生成：双阈值分割法与GrabCut前后景分割算法。其中，当前景（叶片）与背景对比度较高的图像（除第6、13、15、25号视频外的所有视频）采用双阈值分割法处理；而对于背景极为复杂、包含多种不同颜色与纹理物体的图像（第6、13、15、25号视频），则采用GrabCut算法进行处理。

联系方式：[e-mail]

搜集汇总

背景与挑战

背景概述

该数据集包含29个风力涡轮机叶片检查视频，用于AI驱动的叶片识别和结构健康监测。数据集提供了视频帧和对应的掩码，分为训练/验证和测试集，并采用了两种不同的图像分割技术来处理复杂背景。