###### Abstract  
Wind turbine blades (WTBs) are prone to damage from their working environment, including surface peeling and cracks. Early and effective detection of surface defects on WTBs can avoid complex and costly repairs and serious safety hazards. Traditional object detection methods have disadvantages of insufficient detection capabilities, extended model inference times, low recognition accuracy for small objects, and elongated strip defects within WTB datasets. In light of these challenges, a novel model named DCW-YOLO for surface damage detection of WTBs is proposed in this research, which leverages image data collected by unmanned aerial vehicles (UAVs) and the YOLOv8 algorithm for image analysis. Firstly, Dynamic Separable Convolution (DSConv) is introduced into the C2f module of YOLOv8, allowing the model to more effectively focus on the geometric structural details associated with damage on WTBs. Secondly, the upsampling method is replaced with the content-aware reassembly of features (CARAFE), which significantly minimizes the degradation of image characteristics throughout the upsampling process and boosts the network's ability to extract features. Finally, the loss function is substituted with the WIoU (Wise-IoU) strategy. This strategy allows for a more accurate regression of the target bounding boxes and helps to improve the reliability in the localization of WTBs damages, especially for low-quality examples. This model demonstrates a notable superiority in surface damage detection of WTBs compared to the original YOLOv8n and has achieved a substantial improvement in the [EMAIL_ADDRESS] metric, rising from 91.4% to 93.8%. Furthermore, in the more rigorous [EMAIL_ADDRESS]-0.95 metric, it has also seen an increase from 68.9% to 71.2%. Wireless  
potentially susceptible to damage over prolonged operational periods [5]. Although early damage may not initially affect the stable operation of wind turbines directly, surface defects that are not promptly repaired have the potential to gradually expand, causing further harm to the structural integrity of the blades and eventually posing a serious risk to their service life [6]. Therefore, employing efficient methods for detecting superficial defects on WTBs and promptly carrying out repairs is a critical strategy for minimizing maintenance expenses and prolonging the operational life of turbines. Simultaneously, it ensures that wind power plants can maintain efficient and continuous power generation. Large-scale wind turbines are predominantly situated in remote and isolated regions, and their blades rotate at an altitude of 80 m or more [7]. This results in significant safety risks and lower efficiency in conventional manual inspections. Furthermore, such inspections must be conducted while the wind turbine is in a shutdown state, severely impacting its power generation efficiency [8]. In recent years, with the rapid development of deep learning technology, deep learning algorithms have been widely applied in fields such as damage detection [9; 10; 11]. One-stage and two-stage detectors are the two primary types of object detection models in use today. Representative models of the two-stage approach include Faster R-CNN [12], Mask R-CNN [13], and others. These models initially generate candidate bounding boxes, followed by classification and regression on each candidate to accomplish object detection. Representatives of one-stage models include the Single Shot MultiBox Detector (SSD) [14] algorithm and the YOLO algorithm series. The SSD algorithm immediately determines the targets' positions and categories on the feature maps. The YOLO series divides the image into grids and predicts bounding boxes and categories for targets in each grid cell. The YOLO family of models is widely favored for their real-time capabilities and high precision. Despite the significant advancements in accuracy brought forth by deep learning-based detection algorithms, their performance is largely contingent upon the quality of the input images.