###### Abstract
Considering large-scale wind turbine images collected via UAVs, the damaged areas often manifest as small-scale features, leading to common instances of missed detections. Furthermore, during the process of image capture, light refraction and other visual disturbances inevitably reduce the recognizability of damage features. Particularly for cases of minor surface damage, these factors further increase the complexity of detection. Therefore, selecting an appropriate time for shooting is crucial. It is recommended to capture images during periods without precipitation, such as on clear evenings or at night, to effectively avoid optical interference and reflections caused by rain, thereby improving image clarity and detection accuracy. This research is based on the newest algorithm in the YOLO series, YOLOv8. In order to enhance the detection performance of the algorithm, a series of improvements have been made. Firstly, inspired by tubular structure segmentation techniques based on topological geometric constraints, this research has integrated DSConv into the cross-scale feature fusion C2f module of YOLOv8, reinforcing the algorithm's focus on damage geometric structures within WTBs images. Secondly, a lightweight upsampling module--the CARAFEs introduced, which preserves high-resolution details while reassembling and expanding features, aiming to enhance the detection accuracy of small-area, superficial damages in large-scale wind turbine images. Finally, the loss function of YOLOv8 has been optimized through the integration of the WIoUv3 strategy. This method combines a dynamic non-monotonic focusing mechanism with a prudent gradient gain allocation strategy, enabling more accurate bounding box regression, particularly for low-quality examples. Building upon the aforementioned optimization techniques, the goal of the research is to improve damage detection accuracy without sacrificing real-time performance. The improved algorithm proposed in this research holds significant research value and practical implications for addressing issues of damage overlap and small-area damage occurrences in large-scale WTB images under complex lighting conditions. The following is a summary of this article's primary contributions. In the C2F module, we have incorporated DSConv, enabling the model to better focus on the geometric structures within WTB images, thus enhancing the accuracy of detecting small-area damages and intricate cracks. 2. Replacing the upsampling method with CARAFE effectively reduces the loss of image feature information during the upsampling process, enhancing the feature extraction capability of the feature enhancement network. 3. The loss function is optimized by the WIoUV3 strategy. This strategy enables more accurate regression of target bounding boxes, rendering them more reliable in scenarios of WTBs damage localization. The remainder of this paper is structured as follows: An overview of the current WTB damage detection methods is given in Section 2. Section 3 presents the detailed description of the proposed improved method. Section 4 introduces the experiment details, including dataset preparation, parameter settings, and evaluation metrics, and demonstrates the efficacy of the suggested method through experiments on a self-made WTB damage dataset. Finally, some conclusions are drawn in Section 5.