## 4 Experiment
### Results and Analysis
#### 4.3.2 Comparison Experiment  
To evaluate the impact of input data quantity on the algorithm's performance, we designed a comparative experiment, training the algorithm with different amounts of input images. Specifically, we used a smaller amount of input data (e.g., 400 images) and larger amounts (e.g., 600 and 800 images) to observe their effects on the model's convergence, generalization ability, and detection accuracy. The performance evaluation metrics used included P, R, [EMAIL_ADDRESS], [EMAIL_ADDRESS]-0.95, and training time. Table 2 displays the outcomes of the experiment. As shown in the table above, with the increase in the number of original training images, the algorithm's mAP value gradually improves. However, when fewer training images are used, the mAP value is significantly lower, and the model's performance  
Figure 8: Detection performance of the model: (**a**) the detection performance of YOLOv8; (**b**) the detection performance of DCW-YOLO. fluctuates, indicating insufficient training data. This lack of data prevents the model from fully learning the important features in the dataset, thus affecting detection performance. Through experiments, we found that when the number of original training images reaches approximately 600, the algorithm achieves optimal output performance. At this data volume, the mAP curve stabilizes, showing that the model has reached a good level of convergence with strong detection capability and generalization performance. Further increasing the number of images does not result in a significant improvement in mAP, indicating that 600 images provide the algorithm with sufficiently rich samples to ensure stable performance across diverse scenarios. However, this also leads to significantly longer training times and higher computational resource consumption. Therefore, 600 images represent the optimal balance between performance and efficiency, meeting the needs of practical applications. To further validate the superiority of the proposed model compared to other mainstream object detection network models, a comparative experiment was carried out using a self-made WTBs damage dataset between the proposed DCW-YOLO algorithm and other models including Faster R-CNN, SSD, Yolov5, Yolov6 [46], Yolov7-Tiny, Yolov8n, YOLOv9 [47], YOLOv10 [48] and RT-DETR [49]. The performance evaluation metrics used were P, R, [EMAIL_ADDRESS], and [EMAIL_ADDRESS]-0.95. Table 3 displays the outcomes of the experiment. Firstly, data from Table 3 clearly demonstrate that YOLOv8 surpasses traditional object detection frameworks like Faster R-CNN in metrics such as recall and mean average precision. This improvement is primarily due to YOLOv8's superior integration of multi-scale features within its feature extraction network, enhancing robustness in detecting objects of varying sizes. In contrast, the SSD algorithm, despite utilizing multiple anchor boxes for different scales, struggles with targets having inconsistent aspect ratios. YOLOv8's enhancements in network architecture and training strategies have significantly boosted its performance in detecting these challenging targets, resulting in superior outcomes. Moreover, YOLOv8 maintains a significantly lower parameter and computational cost compared to traditional detection algorithms, achieving greater efficiency. Secondly, compared to other iterations in the YOLO series such as YOLOv6, YOLOv7-Tiny, and YOLOv10, YOLOv8 continues to exhibit advantages in various detection metrics. The relatively shallow network architecture of YOLOv6 limits its learning and expressive capabilities, thus hindering its performance in complex scenarios. YOLOv7-Tiny, on the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Input Images** & **P** & **R** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]–0.95** & **Training Time (Hours)** \\ \hline
400 & 0.939 & 0.704 & 0.782 & 0.508 & 0.233 \\
600 & 0.936 & 0.871 & 0.914 & 0.689 & 0.676 \\
800 & 0.918 & 0.826 & 0.893 & 0.635 & 0.727 \\ \hline \hline \end{tabular}
\end{table}