### Related Works  
Numerous academics have focused on the damage identification of wind turbine blades. Some have utilized fiber optic sensing, thermography, shearography, acoustic sequences, and other techniques. For example, [PERSON] et al. [5] used fiber optic sensing techniques. Wind turbine blades had in situ triboluminescent optical fiber sensors embedded in the adhesive layer. The in situ triboluminescent optical fiber sensors can detect damage near the blade adhesive joint in real-time, according to tests. Long-pulse thermography was proposed by [PERSON] [6] et al. for detecting coating peeling and material damage defects in wind turbine blades. Despite the non-vertical detection view, this technology can guarantee good detection results. The shearography was improved by [PERSON] [7] et al. A novel shearography system based on a simple setup of Michelson interferometry using pixelated spatial phase shift was used in the study for the dynamic inspection of delamination defects on a real wind turbine blade. The obtained results show that the proposed approach is capable of accurately detecting defects and dynamically deriving phase-changing sequences. [PERSON] [8] et al. conducted an acoustic array study by placing an audio speaker inside a wind blade to collect and observe the sound emitted by the blade to detect structural damage. The methods for identifying wind turbine blade damage proposed from the above perspectives have good accuracy rates, but they often have limitations. Some methods are expensive and time-consuming, and some can only detect a single damage defect, making them unsuitable for use in the actual industrial environment. Later, some academics proposed machine learning-based methods for damage detection of the wind turbine blade. For example, [PERSON] et al. [9] used the decision tree approach in machine learning to obtain the desired features by recording the vibration signals of the leaves based on their various damage states using histogram features. [PERSON] et al. [10] proposed a wind turbine blade fatigue damage diagnosis method based on a Bayesian classifier, which extracted the input features required for the Bayesian framework from the vibration signal of the blade and then identified the wind turbine blade damage based on the output of the classifier. [PERSON] et al. [11] investigated a new machine-learning technique for damage detection of operating wind turbine blades. Using structural health monitoring techniques and Gaussian processes, damage to the blade was determined and identified. [PERSON] et al. [12] proposed a data-driven framework for the automatic detection of wind turbine blade surface cracks based on UAV images. Haar-like features are used to describe the crack regions and a cascade classifier is trained to detect the cracks. To sum up, methods based on machine learning are frequently straightforward and efficient, but they cannot automatically extract flawed characteristics and typically demand a great deal of preparation. Recently, convolutional neural networks have been widely used in detection techniques due to their advantages of simple operation, automatic extraction of feature information, and high identification efficiency. [PERSON] et al. [13] proposed an effective method for multi-feature fusion using a one-dimensional convolutional neural network model. It was used to improve the detection performance of low signal-to-noise ratio magnetic anomaly signals. [PERSON] et al. [14] designed a portable vision sensor system using the simplified and optimized lightweight model MobileNet_SSD for detecting spray deposition in UAV applications. [PERSON] et al. [15] proposed a novel generative adversarial network (GAN)-based fall detection method using a heart rate sensor and an accelerometer. Thus, some academics have employed deep learning techniques to address this problem. [PERSON] et al. [16] proposed an approach using the deep learning method.