## 4 Experiment
### Results and Analysis  
#### 4.3.1 Ablation Experiment  
Three improvement methods, named D (DSConv), C (CARAFE), and W (WIoUv3), were proposed in the experiment. To verify the efficacy of these three enhancement methods, all ablation experiments were conducted on our self-made dataset of damaged fan blades. The evaluation metrics for detection performance included \(\mathrm{P}\), \(\mathrm{R}\), [EMAIL_ADDRESS], and [EMAIL_ADDRESS]-0.95. Use Params and GFLOPs as evaluation metrics for model lightweighting. As shown in Table 1, using the original YolovSn model as a baseline, the effectiveness of each improvement method is evaluated individually by adding them one at a time. This approach reveals the specific impact of each improvement on model performance. Moreover, by combining different improvement strategies, it is possible not only to assess the effect of individual improvements but also to explore whether there is a synergistic effect between them, thereby further enhancing the performance of the model. Through these experiments, we can better evaluate the effect of each improvement method on the performance enhancement of object detection algorithms. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**DSConv** & **CARAFE** & **WIoUv3** & **P** & **R** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]-0.95** & **Params (M)** & **GFLOPs (G)** \\ \hline \multirow{4}{*}{\(\surd\)} & & & 0.936 & 0.871 & 0.914 & 0.689 & 3.01 & 8.1 \\  & & & 0.943 & 0.876 & 0.927 & 0.692 & 3.31 & 8.5 \\  & \(\surd\) & & 0.944 & 0.882 & 0.923 & 0.698 & 3.14 & 8.6 \\  & & \(\surd\) & 0.938 & 0.875 & 0.925 & 0.69 The symbol \"\(\sqrt{\text{\tiny{\text{\textbullet}}}}\)\" indicates the inclusion of the improvement method. As we can see from Table 1, compared to the original Yolov8n, the most significant improvement was observed by incorporating dynamic snake convolution into the C2f module at the neck end, with an increase of 1.3% in [EMAIL_ADDRESS] and 0.3% in [EMAIL_ADDRESS]-0.95. Further enhancement in detection accuracy was achieved through improving the upsampling, leading to a rise of 0.9% in [EMAIL_ADDRESS] and 0.9% in [EMAIL_ADDRESS]-0.95. Following loss function optimization, the bounding box regression became more accurate, leading to final [EMAIL_ADDRESS] and [EMAIL_ADDRESS]-0.95 reaching 92.5% and 69%, respectively. The tabular data also indicates that integrating any two improvement methods can enhance detection performances. Ultimately, after integrating three enhancement methods, the model's parameter count and computational complexity are 3.35M and 8.5 GFLOPs, respectively. With only a minor increase in computational overhead, the DCW-YOLO model achieved significant performance improvements. Its [EMAIL_ADDRESS] index reached 93.7%. The precision could reach up to 96.8%, and the recall rate up to 89%. The [EMAIL_ADDRESS] increased from the initial 91.4% to 93.7%, and the [EMAIL_ADDRESS]-0.95 rose from the initial 68.9% to 71.2%. Figure 7 illustrates the comparison of mAP values obtained on the wind turbine blade damage dataset between the original YOLOv8 algorithm and its enhanced version, the DCW-YOLO model. From Figure 7, it can be observed that the improved DCW-YOLO model achieves a higher mAP compared to the original YOLOv8 model, with an increase from 91.4% to 93.7%. Specifically, for crack damage, the mAP increases from the original 95.5% to 97%, and for peeling damage, the mAP increases from the original 87.2% to 90.3%. To vividly demonstrate the effectiveness of the improved algorithm in practical applications, it was compared with the original YOLOv8 algorithm on a WTBs dataset. Two types of damaged image samples, captured under different background complexities and lighting conditions, were selected for comparison. As confirmed by the detection results shown in Figure 8, the improved algorithm significantly outperforms the original YOLOv8 algorithm in terms of detection confidence and accuracy in identifying blade damage.