Wind turbine generator blade unmanned aerial vehicle inspection defect detection method and device and storage medium

A technology for defect detection and wind turbines, applied in neural learning methods, image data processing, image enhancement, etc., can solve a large number of artificially designed features and other problems, and achieve high recognition accuracy and real-time effects

Pending Publication Date: 2021-12-21
国电电力宁夏新能源开发有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Moreover, traditional computer vision algorithms require a large number of manually designed features, and the designed features also require a lot of debugging work

Method used

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  • Wind turbine generator blade unmanned aerial vehicle inspection defect detection method and device and storage medium
  • Wind turbine generator blade unmanned aerial vehicle inspection defect detection method and device and storage medium
  • Wind turbine generator blade unmanned aerial vehicle inspection defect detection method and device and storage medium

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Embodiment 1

[0054] See figure 1 , figure 1 It is a flowchart of a defect detection method for wind turbine blade UAV patrol inspection provided by an embodiment of the present invention. As shown, the method includes:

[0055] Step1: Use the UAV to inspect and collect the original image of the blade of the wind turbine, and segment the original image along the direction of the blade to obtain several sub-images including the blade area;

[0056] Step2: Input several segmented sub-images into the leaf defect detection network model for leaf defect detection, and obtain the defect information of the segmented sub-images. The defect information includes the defect type and the coordinates of the defect detection frame;

[0057] Step3: According to the defect information of the segmented sub-image, the type and position of the defect on the blade of the wind turbine are obtained.

[0058] Specifically, in this embodiment, the UAV is equipped with a camera, and the UAV is controlled to take...

Embodiment 2

[0131] On the basis of the above embodiments, this embodiment provides a wind turbine blade UAV inspection defect detection device for implementing the wind turbine blade UAV inspection defect detection method in Embodiment 1.

[0132] See Figure 11 , Figure 11 It is a structural block diagram of a wind turbine blade UAV inspection defect detection device provided by an embodiment of the present invention. As shown in the figure, the device of this embodiment includes: an image acquisition module 10, an image segmentation module 20, a blade defect detection module 30 and a data processing module 40.

[0133] Wherein, the image acquisition module 10 is used to acquire the original image of the wind turbine blade. The image segmentation module 20 is used to perform image segmentation on the original image along the direction of the blade to obtain several sub-images that include the blade area. It should be noted that the spliced ​​image of the several sub-images includes th...

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Abstract

The invention relates to a wind turbine generator blade unmanned aerial vehicle inspection defect detection method and device and a storage medium. The method comprises the following steps: utilizing an unmanned aerial vehicle to inspect and collect an original image of a wind turbine generator blade, and carrying out image segmentation on the original image along the blade direction to obtain a plurality of segmented sub-images containing blade areas; inputting the plurality of segmented sub-images into a blade defect detection network model, and performing blade defect detection to obtain defect information of the segmented sub-images; and according to the defect information of the segmented sub-images, obtaining the type and the position of the defect on the wind turbine generator blade, wherein the blade defect detection network model is obtained by constructing a blade defect detection network and performing network training, and the blade defect detection network adopts a Yolo v4 neural network. According to the method, the Yolo V4 neural network is adopted for identifying the major defects of the blade, and compared with a traditional computer vision algorithm, the method is more stable.

Description

technical field [0001] The invention belongs to the technical field of wind power equipment detection, and in particular relates to a defect detection method, device and storage medium of a wind turbine blade UAV patrol inspection defect detection method. Background technique [0002] In the field of wind power generation, the blades of wind turbines are one of the important parts that convert wind energy into electrical energy. However, in the process of wind turbine power generation, due to factors such as changing environments, common defects such as sand holes, cracks, and peeling may occur on the surface of the blades. , these defects will seriously affect the efficiency and safety of wind power generation. [0003] At present, the detection methods of fan blades mainly include visual method, ultrasonic detection, traditional computer vision algorithm, acoustic emission detection, and infrared thermal imaging technology. Ultrasonic testing can locate and quantitatively...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/11G06N3/04G06N3/08
CPCG06T7/0002G06T7/11G06N3/08G06T2207/10004G06T2207/20081G06T2207/20084G06T2207/20021G06T2207/20221G06N3/045Y04S10/50
Inventor 李刚邹学李骥赵剑寒李小慧刘俊燕渠叶君
Owner 国电电力宁夏新能源开发有限公司
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