## 4 Experiment  
### Data Collection and Preparation  
The dataset was collected using a high-resolution drone, specifically the DJI Mavic 2 Pro, equipped with a Hasselblad L1D-20c camera. This camera features a 20-megapixel 1-inch CMOS sensor with dimensions of 13.2 mm (width) \(\times\) 8.8 mm (height), capable of capturing greater detail and richer color information. The lens has a 28 mm equivalent focal length, and the image resolution is 5472 \(\times\) 3648 pixels. During the image capture process, the distance between the drone and the WTBs was approximately 5 to 15 m, ensuring precise detection of surface defects. According to imaging principles, the ratio of the target's actual size to the sensor size is equal to the ratio of the shooting distance to the focal length. The following formula can be used to calculate the physical size of each pixel in the scene. Assuming a target distance of \(D\) meters, a focal length of \(f\), a sensor width of \(W\), and an image width resolution of \(X\), the physical size per pixel \(S\) can be expressed using the following formula.  
\[S=\frac{W\times D}{f\times X} \tag{16}\]  
Thus, at a shooting distance of 5 m, the pixel size is approximately 0.43 mm. This means that each pixel represents a physical size of 0.43 mm in the actual scene, providing sufficient resolution to accurately detect and classify both peeling and crack defects. In evaluating the model's performance, determining the minimum detectable defect size is crucial. The minimum detectable defect size primarily depends on image resolution, the distance between the target and the camera, and the downsampling factor of the model's feature extraction. Typically, the downsampling factor of the final detection layer in YOLOv8 is 32, meaning that each pixel in the feature map corresponds to a \(32\times 32\) pixel area in the original image. Assuming a downsampling factor of \(\sigma\), the minimum detectable size \(M\) can be expressed using the following formula.  
\[M=S\times\sigma \tag{17}\]  
By substituting the previously obtained minimum physical size per pixel (0.43 mm), we can determine the minimum defect size that the model can detect. Therefore, at a  
Figure 5: The structure of DCW-YOLO model.  
shooting distance of 5 m, the model can detect a minimum defect size of approximately 13.76 mm.  
With image samples obtained from two wind farms in Liaoning and Jiangsu provinces, China. A total of 600 high-definition original damage images were collated. After utilizing 600 original images and applying image preprocessing, the sample size is sufficient for binary damage detection, effectively supporting the training and validation of the algorithm. To enhance data diversity and facilitate effective training of deep learning models, the original photos were subjected to a series of data augmentation processes, including image flipping, rotation, and HSV color transformation. Through these preprocessing operations, the images were expanded to 3550 and then divided in a 9:1 ratio into a training set and a validation set. The dataset primarily consists of two types of damage: cracks and peelings. As shown in Figure 6, a subset of the dataset collected in this experiment is displayed.  
In this experiment, training was performed using a GPU. The operating system used was Ubuntu 20.04, with an RTX 3080 graphics card and an Intel(R) Xeon(R) Platinum 8255C CPU. The CUDA version used was 11.8. The deep learning framework utilized was PyTorch 2.0.0, and the programming language used was Python 3.8. The resolution of the training images was set to 640 \(\times\) 640, with a batch size of 16. The training was performed for 100 iterations, utilizing a stochastic gradient descent (SGD) optimizer, a learning rate of 0.01; a momentum parameter of 0.937; a weight decay value of 0.0005.