UAV-based inspection of wind turbine blade surface defects detection technology
Under the background of “double carbon”, it is particularly important to vigorously develop new energy. Wind power generation is an important clean energy, and the scale of wind power is also expanding in the field of new energy. With the increasing scale of wind turbines, the damage probability of...
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| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Harbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd.
2025-03-01
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| Series: | Diance yu yibiao |
| Subjects: | |
| Online Access: | http://www.emijournal.net/dcyyb/ch/reader/create_pdf.aspx?file_no=20220611005&flag=1&journal_id=dcyyb&year_id=2025 |
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| Summary: | Under the background of “double carbon”, it is particularly important to vigorously develop new energy. Wind power generation is an important clean energy, and the scale of wind power is also expanding in the field of new energy. With the increasing scale of wind turbines, the damage probability of blades is also increasing. Aiming at the problems of high cost and poor working environment of large-scale wind turbine blade defect detection, a wind turbine blade surface defect detection method based on UAV image acquisition and digital image processing is proposed in this paper. According to the characteristics of images collected by UAV, this paper adopts the weighted average method to realize gray processing, and then, the median filtering is applied to realize image noise reduction; the image enhancement is realized by CLAHE algorithm, which makes the details of target area and defect more clear and complete, and improves the detection efficiency. The feature information of defect is separated and extracted through image foreground segmentation and threshold processing, and the connected domain is framed to realize the detection of blade surface. The accuracy and error rate of defect images is calculated and tested by introducing performance evaluation index MIoU. The experimental results show that the detection accuracy of the proposed method for typical blade defects such as trachoma, scratch and crack is above 90%, especially the detection accuracy of crack defects can reach 95%, which verifies the effectiveness and accuracy of the algorithm in blade detection. |
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| ISSN: | 1001-1390 |