Defect Detection on a Wind Turbine Blade Based on Digital Image Processing

Deng, Liwei; Guo, Yangang; Chai, Borong

doi:10.3390/pr9081452

Cited by 36 publications

(11 citation statements)

References 15 publications

(19 reference statements)

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“…In the complex environment of the inland river, the water surface can be effectively segmented out using the improved semantic segmentation model. Based on segmenting out the water surface area, the edges of the water surface area are extracted by combining the classical edge detection Canny algorithm [31], and the extracted edge lines are superimposed onto the original image after expansion [32] processing to facilitate observation. The results are shown in Fig.…”

Section: The Water Shoreline Detectionmentioning

confidence: 99%

Improved PSPNet-based water shoreline detection in complex inland river scenarios

Yin

Guo²,

Deng³

et al. 2022

Complex Intell. Syst.

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The water shoreline is essential for unmanned surface vessels (USVs) to navigate autonomously. Many existing traditional water shoreline detections approaches not only fail to overcome the effects of water reflections, image inversions, and other factors but are also unsuitable for water shoreline detection in a variety of weather conditions and in complex inland river scenarios. Therefore, we propose a water shoreline detection approach based on an enhanced Pyramid Scene Parsing Network (PSPNet). We introduce a migration learning approach to the PSPNet feature backbone extraction network Resnet50 to improve training efficiency and add a Convolutional Block Attention Module (CBAM) attention mechanism module to improve the robustness of training. In addition, the pyramid pooling module adds the branch of the atrous convolution module. Finally, the waterfront segmentation map is processed by the Canny edge detection method, which detects the water shorelines. For the network's training and validation, we use the USVInland dataset, the world's first urban inland driverless dataset. The experimental results show that the segmentation accuracy MIou of this paper is 96.87% and Accuracy is 98.41, which are higher than some mainstream algorithms. It is capable of detecting water shorelines accurately in a variety of interior river situations.

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Section: The Water Shoreline Detectionmentioning

confidence: 99%

Improved PSPNet-based water shoreline detection in complex inland river scenarios

Yin

Guo²,

Deng³

et al. 2022

Complex Intell. Syst.

Self Cite

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“…The current application of wind turbine blade damage detection technology mainly includes strain measurement, ultrasound, acoustic emission, vibration, thermal imaging [3], machine vision, etc. Deng Liwei et al detected wind turbine blade defects based on digital image processing [4], firstly, the LPSO algorithm and logarithmic Gabor filter were used to generate an adaptive filter, and were able to directly output the optimal image containing the results of multiple feature extractions. Secondly, Hog features combined with SVM classifier are utilized to identify and classify the defect types.…”

Section: Introductionmentioning

confidence: 99%

Identifying wind turbine blade defects using YOLOv5 and YOLOv8 deep learning algorithms

Yang,

Wang

2024

Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023)

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Wind power generation solves global energy supply and demand problems and promotes energy conservation, environmental protection and sustainable development. Accurate detection and identification of wind turbine blade defects is essential for the maintenance and upkeep of wind power systems. In this paper, four versions of You Only Look Once (YOLO) object recognition algorithm (YOLOv5n, YOLOv5s, YOLOv8n, YOLOv8s) are evaluated for wind turbine blade defect detection. The construction of wind turbine blade defect dataset is completed using image enhancement by flipping, rotating, colour changing, and noise injection, and a target detection model is trained using the YOLO model variant, which is able to quickly and accurately detect the defects on the blades, including soiling, oil leakage, erosion, and gelcoat peeling. The experimental results show that YOLOv5n, YOLOv5s, YOLOv8n, and YOLOv8s were trained for about 12.87, 11.32, 14.01, and 31.29 hours, the best mAP@0.5:0.95 is 0.83, 0.86, 0.93 and 0.95 respectively. Utilizing YOLOv5n, YOLOv5s, YOLOv8n, and YOLOv5s trained models to detect the same picture of wind power defects are about 77ms, 179ms, 139ms, and 261ms, respectively. In this study, YOLOv8n achieves the optimal combination of average accuracy and speed, which should be considered for wind blade defect detection and can improve the safety and reliability of wind power generation systems.

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“…With the further development of image processing technology, image segmentation, stitching, fusion and other technologies are applied to the research of blade damage detection. Many feasible methods are proposed and perform well, for example, the improved LPSO algorithm and log-Gabor adaptive filter are proposed to extract multiple defect features in the image [21], Haar-like features are used to locate cracks and extracting crack pixels by Jaya K-means algorithm [22], dynamic splicing technology is used to obtain high-precision blade vibration characteristics after DIC measurement [23], infrared and visible images are fused to enhance image features [24,25]. In general, most of the existing studies focus on modeling a few kinds of facilely distinguishable damage, such as cracks, sand holes, scratches, etc, and deal with wind turbine blade images with good shooting conditions, simple backgrounds and high contrast.…”

Section: Introductionmentioning

confidence: 99%

Multiple damage segmentation and extraction of wind turbine blades surface under complex background based on SKRT approach

Wang,

Zheng,

Lang

et al. 2023

Meas. Sci. Technol.

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Machine vision detection technology has been widely used in detecting wind turbine blade surface damage, but the complex background often has a significant impact on blade damage detection. Aiming at the complex background blade images caused by various natural background features, complex blade surface features, low contrast features, non-uniform illumination, weather conditions, and other factors, the work proposes an SKRT segmentation method based on K-means clustering and adaptive threshold fusion to segment and extract multiple damage features on the surface of wind turbine blades with complex backgrounds. Firstly, an single-scale retinex(SSR) algorithm is adopted to enhance the contrast between surface damage and image background in the research. Then the image is roughly segmented by K-means clustering to remove most of the background features, and the background mask is replaced with the peak of the remaining feature image pixels to solve the problem of wrong segmentation. Finally, making use of the adaptive threshold local segmentation method to accurately segment the blade surface damage. The experimental results show that the proposed SKRT segmentation method can significantly improve the segmentation accuracy of wind turbine blade surface damage with complex background, and the accuracy, intersection over union, and F-measure value are increased by 37.20%, 35.71%, and 28.69%, respectively. The method performs better robustness to multiple damage feature segmentation.

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Defect Detection on a Wind Turbine Blade Based on Digital Image Processing

Cited by 36 publications

References 15 publications

Improved PSPNet-based water shoreline detection in complex inland river scenarios

Improved PSPNet-based water shoreline detection in complex inland river scenarios

Identifying wind turbine blade defects using YOLOv5 and YOLOv8 deep learning algorithms

Multiple damage segmentation and extraction of wind turbine blades surface under complex background based on SKRT approach

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