SURFACE DAMAGE DETECTION AND RECOGNITION OF WIND TURBINE BLADE BASED ON IMPROVED YOLO-v3

Jiang Xingqun; Liu Bo; Song Li; Jiao Xiaofeng; Feng Rui; Chen Yongyan

doi:10.19912/j.0254-0096.tynxb.2021-1317

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (3) : 212-217. DOI: 10.19912/j.0254-0096.tynxb.2021-1317

SURFACE DAMAGE DETECTION AND RECOGNITION OF WIND TURBINE BLADE BASED ON IMPROVED YOLO-v3

Jiang Xingqun¹, Liu Bo², Song Li^1,3,4, Jiao Xiaofeng⁵, Feng Rui⁶, Chen Yongyan^1,3,4

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Abstract

Wind turbine blades is one of the important components for capturing wind energy, and the health of blades affects the performance of the whole generator set. To detect the damage status of blades effectively, a wind turbine blade damage detection and identification technique is proposed based on the improved YOLO-v3 algorithm. According to the characteristics of wind turbine blade damage area, the scale of anchor frame in the network is adjusted and optimized. The squeeze and excitation networks (SENet) structure based on the attention mechanism is introduced after the feature extraction network to make the YOLO-v3 algorithm focus more on the target-related feature channels and improve the network performance. Results show that the MAP (mean average precision) value of the improved YOLO-v3 algorithm is 84.42%, which is 6.14% higher than the original YOLO-v3algorithm, and the detection time is reduced by 21 ms, the improved YOLO-v3 algorithm can better identify the surface damage of blades.

Key words

wind turbines / blades / damage detection / deep learning / target detection / YOLO-v3

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Jiang Xingqun, Liu Bo, Song Li, Jiao Xiaofeng, Feng Rui, Chen Yongyan. SURFACE DAMAGE DETECTION AND RECOGNITION OF WIND TURBINE BLADE BASED ON IMPROVED YOLO-v3[J]. Acta Energiae Solaris Sinica. 2023, 44(3): 212-217 https://doi.org/10.19912/j.0254-0096.tynxb.2021-1317

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