## 1 Introduction
### Related Works  
Many scholars have been paying constant attention to the problem of WTB surface damage identification, and have proposed many related damage identification methods. At present, the common methods of WTB damage recognition are roughly divided into two categories: the convolutional-neural-network-based damage image recognition approach and the conventional damage recognition method. Traditional damage identification methods mainly use sensors to detect signals of WTBs for identifying damage, including thermal imaging, vibration analysis, acoustic emission, optical fiber sensing, and other recognition technologies. The infrared thermography technique is based on the thermal wave theory, which uses the optical radiation on the blade surface to analyze the heat transfer of various parts of the blade and convert it into a visual graph for analyzing the health of the blades. [PERSON] et al. proposed a continuous-line laser thermography technique [4] and a continuous-wave line laser thermography system [5] for non-destructive WTB monitoring. The system works under rotational conditions by generating thermal waves and using an infrared camera to record the propagation of corresponding waves. In their experiment, the method was able to correctly detect more than 95% of the rotating blade damage. [PERSON] et al. [6] studied two thermography techniques, including passive and active pulsed and step heating and cooling thermography. When the component is damaged, the vibration analysis detection technology analyzes and determines structural damage by displaying frequencies and modes of physical parameters. [PERSON] et al. [7] used the Finite Element Method (FEM) and Laser Scanning Vibrometry (LSV) to determine the size and location of stratification in WTBs. [PERSON] et al. [8] proposed a data-driven vibration-based damage detection method, which uses multivariate Damage Sensitive Features (DSFs) extracted from acceleration responses to conduct damage detection. The experimental results showed that the best damage detection accuracy of 79.2% was obtained using sequential projection pursuit (SPP)-converted DSFs. When WTBs are iced, [PERSON] et al. [9] used a random forest classifier to detect this by analyzing data from wind farms with combined vibration signals in supervisory control and data acquisition systems. Acoustic emission detection technology observes the emissive sound signal when the blade is working normally. When cracks or other mechanical damage occur, the noise signal becomes unstable and time-varying. The highest accuracy of 97.4% was proved. [PERSON] et al. [10] studied the feasibility of in-service monitoring of the structural health of blades by acoustic emission. Additionally, with the proposed lowering of the threshold from 45 dB to 40 dB, the signal-to-threshold ratio would increase from 5 to 10, which would allow an increase in the detection range from 0.35 m to 3 m, thus reducing the number of sensors required to 8 for basic monitoring and 32 for localization. [PERSON] et al. [11] outlined an adaptive wavelet packet denoising algorithm that applies to numerous SHM technologies and includes acoustics, vibrations, and acoustic emission. Additionally, the success rate of damage detection was improved by 60% after applying the proposed adaptive wavelet packet denoising algorithm. [PERSON] et al. [12] performed health monitoring of a 59.5 m long composite WTB under fatigue loads using an Acoustic Emission (AE) technique. Fiber-sensing detection mainly uses fiber gratings, which have good photosensitivity. When the external environment changes, the projected wavelength of the grating changes, so the deformation of the blade is obtained by measuring the change in wavelength. [PERSON] et al. [13] proposed a novel non-baseline defect detection methodology based on fiber Bragg grating (FBG) in WTB. Additionally, the authors provide the results of the mean damage detection error (MDDE) comparison between the proposed method and the conventional strain energy method.