## 5 Conclusions  
This study proposes a WTB damage detection model called DCW-YOLO, which is based on the one-stage object detection model YOLOv8. To enhance the efficacy of the algorithm in identifying complex small targets within extensive images of WTB damage, three improvements are made. Firstly, dynamic snake convolution is integrated into the C2f module before the three detection heads of the YOLOv8 network model. This advancement permits the model to efficiently detect the elongated and curved characteristics of the targets under blur or overlap conditions, enhancing the algorithm's focus on the geometric structure of the damage in WTB images. Secondly, the upsampling method is replaced with CARAFE, which mitigates the issue of minor damaged targets being ignored during upsampling and successfully minimizes feature loss. Finally, the optimization of the loss function is achieved through the integration of the WIoUv3 strategy, It allows for more accurate bounding box regression by combining a sensible gradient gain allocation technique with a dynamic non-monotonic focus mechanism. According to experimental data, the suggested DWC-YOLO model exhibits notable improvements in detection performance metrics when compared to the original YOLOv8 model. Specifically, accuracy is improved by 3.2%, recall  
Figure 10: WTB damage detection results: (**a**) YOLOv5 detection effect. (**b**) YOLOv6 detection effect. (**c**) YOLOv7-Tiny detection effect. (**d**) YOLOv8 detection effect. (**e**) YOLOv10 detection effect. (**f**) DCW-YOLO detection effect. is increased by 1.9%, [EMAIL_ADDRESS] is increased by 2.3%, and [EMAIL_ADDRESS]-0.95 is increased by 2.3%. These experimental results indicate the outstanding performance of the DWC-YOLO model in intelligently identifying WTB damage. This model has important research value and practical significance in dealing with overlapping damage and small-area damage in large-scale WTB images under complex lighting conditions, offering dependable technical assistance for real-world implementations in monitoring the health of wind turbines. However, it must be acknowledged that this study has certain limitations and there is room for further improvement. First, although our method has achieved significant achievements in feature extraction and detection accuracy, the corresponding parameter count and computational load have also slightly increased, which may affect the deployment efficiency of the algorithm on edge devices. Therefore, in future research, we will focus more on exploring the lightweight implementation of the algorithm to optimize its performance in resource-constrained environments. Secondly, subsequent studies will continue to delve into the accurate assessment of damage severity, as accurately determining the extent of blade damage is crucial for maintaining the normal operation of wind turbines. Finally, to enhance the resilience and generalizability of the algorithm, we will continue to refine and adjust it meticulously. Overall, the DCW-YOLO model proposed in this study provides an effective and practical method for detecting WTB damage, offering significant research value and practical significance for monitoring the health of wind turbines. Conceptualization, A.C.; methodology, A.C.; software, A.C.; validation, L.Z. and A.C.; formal analysis, L.Z. ; investigation, L.Z. and A.C.; resources, A.C.; data curation, L.Z. and A.C.; writing--original draft preparation, A.C.; writing--review and editing, L.Z. ; visualization, L.Z. and A.C.; supervision, C.L. ; project administration, X.Y. and Y.S. ; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China under Grant 52005071 and the Applied Basic Research Program Project of Liaoning Province under Grant 2023 JHz/101300236. Not applicable. The data that support the findings of this study are available from the corresponding author upon reasonable request. The authors declare no conflicts of interest.