## 4 Experiment
### Results and Analysis
#### 4.3.2 Comparison Experiment

Table 2: The impact of different numbers of input images on the model. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **P** & **R** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]–0.95** & **Params (M)** & **GFLOPs (G)** \\ \hline Faster R-CNN [12] & 0.903 & 0.807 & 0.813 & 0.560 & 44.68 & 147.6 \\ SSD [14] & 0.899 & 0.796 & 0.805 & 0.547 & 22.85 & 27.7 \\ YOLOv5 & 0.929 & 0.797 & 0.862 & 0.593 & 2.51 & 7.1 \\ YOLOv6 [46] & 0.897 & 0.747 & 0.824 & 0.566 & 4.23 & 11.8 \\ YOLOv7-Tiny [35] & 0.911 & 0.799 & 0.856 & 0.544 & 6.02 & 13.2 \\ RT-DETR [49] & 0.801 & 0.741 & 0.804 & 0.541 & 31.99 & 103.4 \\ YOLOv8 & 0.936 & 0.871 & 0.914 & 0.689 & 3.01 & 8.1 \\ YOLOv9 [47] & 0.954 & 0.842 & 0.902 & 0.668 & 25.32 & 102.3 \\ YOLOv10 [48] & 0.874 & 0.736 & 0.831 & 0.572 & 2.69 & 8.2 \\ DCW-YOLO & 0.968 & 0.890 & 0.937 & 0.712 & 3.35 & 8.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of object detection models. other hand, lacks sensitivity to changes in target sizes, which compromises its efficacy in detecting both small and large objects. YOLOv10's attempt to balance network depth and width has not translated effectively in practical applications, failing to capture sufficient feature representations, particularly in complex scenarios like WTB damage detection. In contrast, YOLOv5, despite its efficiency in terms of parameter count and computational complexity (only requiring 2.51M parameters and 7.1 GFLOPs), falls short in precision compared to YOLOv8, largely due to its anchor-free training approach, which still requires further optimization for detecting smaller targets. Although YOLOv9 achieves a higher precision rate of 0.954, surpassing YOLOv8's 0.936, its [EMAIL_ADDRESS] and [EMAIL_ADDRESS]-0.95 are 0.902 and 0.668, respectively, both lower than YOLOv8's 0.914 and 0.689, indicating that while YOLOv9 excels in accurately identifying targets, YOLOv8 maintains a superior overall performance in both high recall and average precision. Additionally, the increased parameter count and computational demands of YOLOv9 (25.32M parameters and 102.3 GFLOPs) may not be optimal for scenarios with limited computational resources. Thirdly, the detection performance of the RT-DETR model is observed to be unsatisfactory from the table data. This is primarily attributed to the fact that large-scale models like RT-DETR typically require substantial data support to perform optimally. However, the dataset available for WTB damage detection is relatively limited, greatly constraining RT-DETR's effectiveness. In contrast, the YOLOv8 algorithm has proven to be more suitable for WTB damage detection in this study. In summary, YOLOv8 strikes a good balance between resource efficiency and performance. Consequently, this paper selects YOLOv8 as the baseline model for improvements. The enhanced DCW-YOLO model, with a minor increase in computational overhead compared to YOLOv8, achieves substantial performance enhancements, with its [EMAIL_ADDRESS] reaching 93.7%.The Figure 9 shows the mAP values obtained for the different models in the experiment. From Figure 9, it is clear that when compared to other algorithms, the YOLOv8 algorithm has a considerable edge in terms of mAP value, demonstrating the accuracy and effectiveness of the baseline model chosen in this research. Furthermore, the DCW-YOLO model, based on the improvement of YOLOv8, demonstrated outstanding performance in the experiment of WTB damage detection, further confirming the effectiveness and applicability of the model improvement. Figure 9: mAP values for different models.