Fast detection of wind turbine blade damage using Cascade Mask R-DSCNN-aided drone inspection analysis

Diaz, P. M.; Tittus, Paulaiyan

doi:10.1007/s11760-022-02450-6

Cited by 8 publications

(1 citation statement)

References 21 publications

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“…Zhang et al [12] developed the Mask-MRNet network for blade fault detection, which improved detection performance by combining Mask R-CNN-512 and MRNet, selecting DenseNet-121 as the optimal classifier. Diaz et al [13] designed a fast detection model based on Cascade Mask R-CNN, which utilized depthwise separable convolutions and image enhancement techniques to achieve high-accuracy damage detection and instance segmentation.…”

Section: Introductionmentioning

confidence: 99%

WTBD-YOLOv8: An Improved Method for Wind Turbine Generator Defect Detection

Tong,

Fan,

Peng

et al. 2024

Sustainability

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Wind turbine blades are the core components responsible for efficient wind energy conversion and ensuring stability. To address challenges in wind turbine blade damage detection using image processing techniques such as complex image backgrounds, decreased detection performance due to high image resolution, prolonged inference time, and insufficient recognition accuracy, this study introduces an enhanced wind turbine blade damage detection model named WTDB-YOLOv8. Firstly, by incorporating the GhostCBS and DFSB-C2f modules, the aim is to reduce the number of model parameters while enhancing feature extraction capability. Secondly, by integrating the MHSA-C2f module, which incorporates a multi-head self-attention mechanism, the focus on global information is enabled, thereby mitigating irrelevant background interference and reducing the impact of complex backgrounds on damage detection. Lastly, adopting the Mini-BiFPN structure improves the retention of features for small target objects in shallow networks and reinforces the propagation of these features in deep networks, thereby enhancing the detection accuracy of small target damage and reducing false negative rates. Through training and testing on the Wind Turbine Blade Damage Dataset (WTBDD), the WTDB-YOLOv8 model achieves an average precision of 98.3%, representing a 2.2 percentage point improvement over the original YOLOv8 model. Particularly noteworthy is the increase in precision from 93.1% to 97.9% in small target damage detection. Moreover, the total parameter count of the model decreases from 3.22 million in YOLOv8 to 1.99 million, marking a reduction of 38.2%. Therefore, the WTDB-YOLOv8 model not only enhances the detection performance and efficiency of wind turbine blade damage but also significantly reduces the model parameter count, showcasing its practical advantages in engineering applications.

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Section: Introductionmentioning

confidence: 99%

WTBD-YOLOv8: An Improved Method for Wind Turbine Generator Defect Detection

Tong,

Fan,

Peng

et al. 2024

Sustainability

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Aero-engine remaining useful life prediction based on a long-term channel self-attention network

Liu,

Chen,

et al. 2023

SIViP

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Early stage damage detection of wind turbine blades based on UAV images and deep learning

Gao,

Ma,

Wang

2023

Journal of Renewable and Sustainable Energy

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In response to the shortcomings of existing image detection algorithms in the early damage detection of wind turbine blades, such as insufficient applicability and unsatisfactory detection results, this paper proposes an improved DINO (DETR with improved denoizing anchor boxes for end-to-end object detection) model for wind turbine blade damage detection called WTB-DINO. The improvement strategy of the DINO model is obtained by collecting and analyzing unmanned aerial vehicle (UAV) daily inspection image data in wind farms. First, the lightweight design of DINO's feature extraction backbone is implemented to meet the requirement of fast and effective video inspection by drones. Based on this, the Focus down-sampling and enhanced channel attention mechanism are incorporated into the model to enhance the feature extraction ability of the Backbone for damaged areas according to the characteristics of wind turbine blade images. Second, a parallel encoder structure is built, and a multi-head attention mechanism is used to model the relationship between samples for each type of damage with uneven distribution in the dataset to improve the feature modeling effect of the model for less-sample damage categories. Experimental results show that the WTB-DINO model achieves a detection precision and recall rate of up to 93.2% and 93.6% for wind turbine blade damage, respectively, while maintaining a high frame rate of 27 frames per second. Therefore, the proposed WTB-DINO model can accurately and in real-time classify and locate damaged areas in wind turbine blade images obtained by UAVs.

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Fast detection of wind turbine blade damage using Cascade Mask R-DSCNN-aided drone inspection analysis

Cited by 8 publications

References 21 publications

WTBD-YOLOv8: An Improved Method for Wind Turbine Generator Defect Detection

WTBD-YOLOv8: An Improved Method for Wind Turbine Generator Defect Detection

Aero-engine remaining useful life prediction based on a long-term channel self-attention network

Early stage damage detection of wind turbine blades based on UAV images and deep learning

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