## 5 Experimental Results and Discussion and Analysis
### Comparative Experiments
Consequently, DAPONet is highly suitable for diverse road damage detection scenarios, including edge computing and mobile deployments. The confusion matrix in Figure 8 illustrates the model's classification performance across various defect types. The model demonstrates strong performance in identifying alligator cracks, manhole covers, and longitudinal patches, achieving accuracies of 0.79, 0.77, and 0.76, respectively. Although the accuracy for detecting longitudinal cracks, transverse cracks, and transverse patches is slightly lower, the model still performs reliably, with accuracies of 0.69, 0.68, and 0.69, respectively. Overall, these results indicate that the model effectively identifies both prominent and subtle defects. The above data highlight the excellent performance of the model in the identification of critical hazards such as cracks, patches and manhole covers. However, for pothole identification and detection, the model is slightly insufficient, with an identification accuracy of only 0.55, and a large portion of the pothole is incorrectly identified as background, which may be due to the difficulty of extracting pothole features from the model, especially when the pothole is highly similar to the surrounding environment in terms of its shape, size, texture, and color, which is easy to be misclassified as background. Meanwhile, the background was also misclassified to varying degrees, which indicates that the model still has a lot of room for improvement in pothole identification as well as background classification. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **P** & **R** & **mAP50** & **mAP 50-95** & **Params** & **FLOPs** & **Model Size** \\ \hline YOLOvSn & 67.0 & 58.4 & 61.7 & 35.8 & 2.5 & 7.1 & 5.3 \\ YOLOvSn & 70.7 & 59.3 & 64.5 & 37.8 & 3.0 & 8.1 & 6.3 \\ YOLOv9t & 69.0 & 57.2 & 60.8 & 36.1 & 2.0 & 7.6 & 4.7 \\ YOLOv10n & 65.0 & 55.6 & 59.7 & 35.8 & 2.7 & 8.2 & 5.8 \\
**DAPONet** & **71.6** & **66.6** & **70.1** & **42.8** & **1.6** & **1.7** & **3.7** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative experimental results of SVRDD dataset test set. Figure 8: The confusion matrix demonstrates the classification performance of the model in identifying various types of defects. As shown in Figure 9, the F1-Confidence Curve demonstrates the performance of the model in identifying various types of hidden dangers under different confidence thresholds. It can be seen through the graph that with the increase in confidence, the value of F1 will first increase and then decrease; this is because the increase in confidence will make the model precision rate gradually increase, so that the model will select the prediction results more strictly, which leads to the exclusion of some correct predictions, and then make the recall rate decrease, and ultimately lead to the decrease in F1. The graph shows that when the confidence level is 0.321, the average F1 score for all categories reaches the maximum value of 0.62. At this point the model finds the optimal balance between precision and recall, and at this confidence level the model is able to carry out the hidden danger identification task with the minimum error rate. As shown in Table 3, in the validation set of MS COCO2017 dataset, in order to verify the performance of the proposed model, in general-purpose scenarios, we compare DAPONet trained using the MS COCO2017 dataset with the lightweight models NanoDet-Plus [58], DPNet [59], PP-PicoDet [60], EfficientDet [61], and the YOLOv5n model. This experiment demonstrates the superior performance of the model for generic object detection.