Method, system and medium for detecting surface defects of wind turbine blades
By applying the depth-separable convolutional structure and channel attention mechanism in YOLOv5 and replacing the Kmeans algorithm with the Kmeans++ algorithm, the problems of low accuracy and slow speed in the detection of surface defects of wind turbine blades are solved, achieving higher accuracy and faster detection effects.
CN119048493BActive Publication Date: 2025-03-28CRRC WIND POWER(SHANDONG) CO LTD +2
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Patent Information
- Application Number
- CN202411514600.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-29
- Publication Date
- 2025-03-28
- Estimated Expiration
- 2044-10-29
AI Technical Summary
Technical Problem
The existing target detection methods have problems such as low accuracy or relatively slow calculation speed in the detection of surface defects of wind turbine blades.
Method used
By replacing the backbone feature in YOLOv5, the convolutional block structure in the extracted network is a depth-separable convolutional structure, and the channel attention mechanism is added to the CSP module, and the Kmeans algorithm is replaced with the Kmeans++ algorithm to improve detection accuracy and speed.
Benefits of technology
It realizes higher detection accuracy and faster calculation speed in the detection of surface defects of wind turbine blades, solving the problems of lower accuracy and slow calculation speed in the prior art.
✦ Generated by Eureka AI based on patent content.
Abstract
The present application discloses a method, system and medium for detecting surface defects of wind turbine blades, mainly relating to the technical field of blade surface defect detection, and is used to solve the problem that the existing target detection methods have low accuracy or relatively slow calculation speed. The method includes: replacing the convolutional block structure in the CBL in the backbone feature extraction network in YOLOv5 with a depth-separable convolutional structure, and adding a channel attention mechanism to the CSP module in the backbone feature extraction network to obtain an updated backbone feature extraction network; adding a channel attention mechanism to the CSP module in the neck structure network in YOLOv5 to obtain an updated neck structure network; replacing the trained Kmeans algorithm in the detection structure network in YOLOv5 with the trained Kmeans++ algorithm to obtain an updated detection structure network; obtaining the final prediction frame through the updated YOLOv5.
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