[1] Li D S, Ho S C M, Song G B et al. A review of damage detection methods for wind turbine blades[J]. Smart Material Structures, 24, 033001(2015).

[2] Slot H M, Gelinck E R M, Rentrop C et al. Leading edge erosion of coated wind turbine blades: review of coating life models[J]. Renewable Energy, 80, 837-848(2015).

[3] Zhao X B, Hou Z F, Wu X et al. Hyperspectral target detection based on transform domain adaptive constrained energy minimization[J]. International Journal of Applied Earth Observation and Geoinformation, 103, 102461(2021).

[4] Zou J, He C, Zhu Y K. Research on the NDT of fan blade based on fiber Bragg grating and wavelet packet energy spectrum technology[J]. Electronic Measurement Technology, 38, 133-138(2015).

[5] Yan H L. Research on surface defect detection algorithm of different color blades based on UAV[J]. Modern Information Technology, 5, 169-172, 175(2021).

[6] Zhang C, Wen C B. Fault detection of wind turbine blade based on improved Mask R-CNN[J]. Renewable Energy Resources, 38, 1181-1186(2020).

[7] Cao J G, Yang G T, Yang X Y. Surface defect detection of wind turbine blades based on RPCA and visual saliency[J]. Journal of Graphics, 40, 704-710(2019).

[8] Zhao X B, Li W, Zhao C H et al. Hyperspectral target detection based on weighted Cauchy distance graph and local adaptive collaborative representation[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 5527313(2022).

[9] Zhao X B, Liu K Q, Gao K et al. Hyperspectral time-series target detection based on spectral perception and spatial-temporal tensor decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 61, 5520812(2023).

[10] Dervilis N, Choi M, Taylor S G et al. On damage diagnosis for a wind turbine blade using pattern recognition[J]. Journal of Sound and Vibration, 333, 1833-1850(2014).

[11] Lian J J, Cai O, Dong X F et al. Health monitoring and safety evaluation of the offshore wind turbine structure: a review and discussion of future development[J]. Sustainability, 11, 494(2019).

[12] Li X Q. Research on image threshold segmentation algorithm based on Matlab[J]. Software Guide, 13, 76-78(2014).

[13] Chlap P, Min H, Vandenberg N et al. A review of medical image data augmentation techniques for deep learning applications[J]. Journal of Medical Imaging and Radiation Oncology, 65, 471-636(2021).

[14] Huang Z Q, Liu X Z, Shi Y et al. Small object detection in road scene based on data augmentation[J]. Journal of Wuhan University of Technology, 44, 79-87(2022).

[15] Wang T Y, Dai Y Q. Denoising algorithms of digital image based on wavelet transform[J]. Journal of Hubei Polytechnic University, 38, 25-30, 55(2022).

[16] Qian Y, Yuan J. Image enhancement of asphalt mixture based on gray-scale transformation and its segmentation treatment[J]. Journal of Transport Information and Safety, 27, 154-157(2009).

[17] Wang Z F, Tang X G. Research on image enhancement method based on gray transform[J]. Science and Technology Innovation Herald, 8, 119(2011).

[18] Du Y, Zhou S X, Jing X J et al. Damage detection techniques for wind turbine blades: a review[J]. Mechanical Systems and Signal Processing, 141, 106445(2020).

[19] Qiu M L, Huang L, Tang B H. ASFF-YOLOv5: multielement detection method for road traffic in UAV images based on multiscale feature fusion[J]. Remote Sensing, 14, 3498(2022).

[20] Li Y, Ni M Z, Lu Y F. Insulator defect detection for power grid based on light correction enhancement and YOLOv5 model[J]. Energy Reports, 8, 807-814(2022).

[21] Li Z Q, Wang Y W, Chen K X et al. Channel pruned YOLOv5-based deep learning approach for rapid and accurate outdoor obstacles detection[EB/OL]. https://arxiv.org/abs/2204.13699v2

[22] Liu X Q, Sun S L. Research on abnormal behavior detection based YOLO network[J]. Electronic Design Engineering, 26, 154-158, 164(2018).

[23] Yang K, Sun Z Y, Wang A H et al. A material defect object detection method based on YOLO network system[J]. Chinese Journal of Systems Science, 28, 70-75(2020).