## 1 Introduction
### Related Works
For single damage detection, the MDDE of the proposed method is 0.12786, and for dual damage detection, the MDDE of the proposed method is 0.024546. These results indicate that the proposed method has excellent accuracy and reliability in detecting wind turbine blade damage. [PERSON] et al. [14] developed a distributed fiber-optic strain sensor for blade monitoring. [PERSON] et al. [15] developed an online monitoring system based on FBG sensors along with fiber optic rotary joints (FORJ). The experimental results of the above traditional damage identification methods are good, but there are environmental and cost problems, so the applications are often unsatisfactory in actual engineering. For example, if the damage occurs close to the blade tip and the natural frequency and vibration response change very little, it is difficult for vibration analysis techniques to identify effectively. Other traditional damage identification methods also have disadvantages, such as difficult operation, harsh environmental conditions, a single type of damage identification, the inability to carry out damage identification during normal operation, and more. In recent years, with the computing power of image processing equipment breaking new highs and the popularity of high-definition UAV, both of them have pushed WTB surface damage recognition technology to a new level, and the damage image recognition method based on a convolutional neural network has gradually become a hot research topic. It has more advantages compared with traditional damage identification methods. For example, it does not require expensive sensing equipment and relieves the cost pressure. It can work in the harsh environmental conditions of the seaside, high altitude, and optical fiber. It automatically distinguishes the difference between different types of damage to achieve a variety of damage categories. It enables damage detection while the wind turbine is running. These advantages have attracted much attention to the damaged image recognition method based on convolutional neural networks in practical deployment. Additionally, it has also attracted more and more scholars to engage in the research of improving this recognition method. [PERSON] et al. [16] proposed a hierarchical identification framework for WTB, which consists of a Harr-AdaBoost step for region suggestion and a convolutional neural network (CNN) classifier for damage detection and fault diagnosis. [PERSON] et al. [17] proposed an image recognition model based on a deep learning network for the automatic extraction of image features and the accurate and efficient detection of WTB damage. [PERSON] et al. [18] proposed a stiffness prediction method based on CNN and LETM networks considering time series stiffness data under the fatigue test. [PERSON] et al. [19] applied the ResNet50 algorithm to identify the surface damage of WTBs based on UAV machine vision. [PERSON] et al. [20] used CNN to predict the icing fault of WTBs. [PERSON] et al. [21] proposed a Wavelet transform and Full CNN (WaveletFCNN) to automatically obtain multi-scale wavelet characteristics from the time domain and frequency domain. [PERSON] et al. [22] proposed an improved Attention and Feature Balanced YOLO (AFB-YOLO) algorithm based on YOLOv5s to enhance the real-time detection of small target defects in WTBs. [PERSON] et al. [23] proposed an image-based fault diagnosis method for WTBs and used CNN for depth feature extractor learning. [PERSON] et al. [24] proposed a vision-based approach for blade tip detecting and positioning. More precisely, they detected each structure of the wind turbine by combining the Mask R-CNN detector and shape constraints. [PERSON] et al. [25] proposed an attention-mechanism-based CNN to classify the common damage to WTBs. Our team proposed ED Net [26] in the early stage.