Drone Footage Wind Turbine Surface Damage Detection

Foster, Ashley; Best, Oscar; Gianni, Mario; Khan, Asiya; Collins, Keri; Sharma, Sanjay

doi:10.1109/ivmsp54334.2022.9816220

Cited by 11 publications

(6 citation statements)

References 20 publications

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“…Foster et al [134] introduced a novel dataset for wind turbine surface damage detection and evaluates the performance of CNNs models, particularly ResNet-101 Faster R-CNN and YOLOv5, in this task. Offshore wind turbine inspection, a traditionally expensive and high-risk operation, can benefit from computer vision techniques, offering reductions in human exposure and costs.…”

Section: Visual Inspection Using Rgb Camerasmentioning

confidence: 99%

Review on the Advancements in Wind Turbine Blade Inspection: Integrating Drone and Deep Learning Technologies for Enhanced Defect Detection

Memari,

Shakya,

Shekaramiz

et al. 2024

IEEE Access

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The increasing demand for wind power requires more frequent inspections to identify defects in the Wind Turbine Blades (WTBs). These defects, if not detected, can compromise the structural integrity and safety of wind turbines. As WTBs are crucial and costly components, they may suffer material degradation and fatigue failure, which affects their performance and safety. Thus, the urgency for efficient and regular monitoring to maintain their structural integrity is greater than ever. This review paper explores innovative methods in fatigue testing, damage detection, and structural reliability in WTBs, focusing on the use of recent inspection methods, including those that take advantage of drones. Drones are used to identify defects such as cracks, erosion, and coating irregularities using high-resolution imagery with the onboard cameras. Various investigators have developed novel data-driven approaches, incorporating machine learning and deep learning, to accurately identify these defects. Although deep learning-based image processing has been successful in other public infrastructure contexts, its application to wind turbine inspection from aerial images presents unique challenges. This paper also highlights the critical role of failure inspection in enhancing the operational integrity of WTBs, showcasing state-of-the-art deep learning techniques that are pivotal for identifying and analyzing failures in WTBs from images captured by drones. The paper provides insights into the latest developments in using drone imagery for blade defect detection, contrasting this method with traditional non-destructive techniques. This approach could significantly transform the wind energy industry by offering a more efficient, automated, and precise way of ensuring the structural health of wind turbines. Unlike previous studies that predominantly focus on isolated aspects such as inspection or fatigue, this review paper not only integrates the three major aspects of WTBs integrity in terms of aerial inspection, image processing using machine learning, and structural integrity of the blade but also undertakes an extensive examination of the prevailing methodologies in the field, pinpointing crucial gaps and challenges. It provides a detailed review of existing research, covering various areas including automated inspection, image processing techniques, fatigue analysis, and the reliability of wind turbines. This approach enriches the discourse by offering a multifaceted perspective on WTB maintenance, thereby advancing the understanding of operational integrity within the field of wind energy.

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Section: Visual Inspection Using Rgb Camerasmentioning

confidence: 99%

Review on the Advancements in Wind Turbine Blade Inspection: Integrating Drone and Deep Learning Technologies for Enhanced Defect Detection

Memari,

Shakya,

Shekaramiz

et al. 2024

IEEE Access

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“…CNNs have displayed superior performance over traditional descriptors, especially with the added advantage of ensemble classifiers [25,26]. Foster et al categorized WTB defects by utilizing image patches for training and inference [17]. Sarkar et al mitigated the challenges around blurry images using a super-resolution CNN model with Laplacian variance preprocessing [27].…”

Section: Related Workmentioning

confidence: 99%

“…However, there is a glaring inconsistency in the training methodology and categories used in these works. We note that benchmarking the performance of WTB surface defect detectors is currently challenging due to the confidentiality of data [25,26] as well as to annotations that are not publicly available even when the data are available [17,23].…”

Section: Related Workmentioning

confidence: 99%

“…Because DTU does not provide surface defect bounding boxes, we initiated an annotation process after conducting a study of the different types of wind turbine blade surface defects noticeable in the database. A number of recent works have used this dataset for the same purpose [17,23,37]; however, they did not disclose their annotations publicly for wider use, and reached different interpretations of the surface defect types. For better reproducibility and benchmarking, our annotations are made available at: https://github.com/imadgohar/DTU-annotations (accessed on 3 May 2023).…”

Section: Datasetmentioning

confidence: 99%

“…However, this does not solve the problem, and certain objects can be poorly represented during model training. Alternatives include dividing the images into smaller patches and then using them directly for training and inference [17]. Figure 1 shows an overview of wind turbine blade (WTB) surface defect inspection using drones and the associated challenges of ultra high-resolution images, making them quite detailed and large.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Slice-Aided Defect Detection in Ultra High-Resolution Wind Turbine Blade Images

Gohar,

Halimi,

See

et al. 2023

Machines

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The processing of aerial images taken by drones is a challenging task due to their high resolution and the presence of small objects. The scale of the objects varies diversely depending on the position of the drone, which can result in loss of information or increased difficulty in detecting small objects. To address this issue, images are either randomly cropped or divided into small patches before training and inference. This paper proposes a defect detection framework that harnesses the advantages of slice-aided inference for small and medium-size damage on the surface of wind turbine blades. This framework enables the comparison of different slicing strategies, including a conventional patch division strategy and a more recent slice-aided hyper-inference, on several state-of-the-art deep neural network baselines for the detection of surface defects in wind turbine blade images. Our experiments provide extensive empirical results, highlighting the benefits of using the slice-aided strategy and the significant improvements made by these networks on an ultra high-resolution drone image dataset.

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