## 5 Experimental Results and Discussion and Analysis
### Comparative Experiments  
Figure 7 shows several representative sets of detection results of each model in real-world scenarios, in order to clearly see the differences in the performance of different models in detecting road hazards. Among them, YOLOv5n is relatively stable in detecting cracks and manhole covers, but it has limited ability in identifying small cracks in some complex scenarios, with a high leakage rate, which indicates that the network structure of YOLOv5n has some limitations in dealing with small-sized objects, especially in feature extraction and fusion, which is not fine enough. In contrast, YOLOv8n has improved its detection accuracy, especially in the detection of long cracks and patches, but there is still a leakage problem in some complex backgrounds, which indicates that the generalization ability of YOLOv8n still needs to be improved, while YOLOv9t, although the detection speed has improved, the detection accuracy decreases in some scenarios, especially for the detection of fine cracks, which indicates that it has sacrificed part of the model's accuracy while improving the detection rate. YOLOv10n is better for the detection of cracks and patches in larger areas, but the phenomena of misdetection and leakage are more obvious in  
Figure 6: At training time, DAPONet’s loss on the SVRDD dataset _al_ong with the evaluation metric iteration plot. some cases. Compared with the above models, it can be seen that DAPONet's performance is significantly superior, its CPDA module can capture multi-scale information of the detection target, thus accurately identifying small cracks, patches, and other such tiny hidden dangers, which makes up for the limitations of YOLOv5n when detecting small-sized objects and, at the same time, with the highly efficient downsampling ability of MCD, it can ensure the model's accuracy while improving the inference speed. It makes up for the shortcomings of YOLOv9t. Moreover, DAPONet can accurately identify various types of road damage in various complex scenarios, and its generalization ability and stability are significantly better than YOLOv8n and YOLOv10n. To assess the performance of the proposed model, we use SVRDD trained YOLOv5, YOLOv8, YOLOv9, YOLOv10 models for comparison, from P, R, mAP50, mAP50-95, Params, FLOPs, Model Size to analyze the model performance, the experimental results are shown in Table 2. It can be seen that DAPONet's P, R, and mAP (mAP50, mAP50-90) are significantly better than other models, reaching 71.6%, 66.6%, 70.1%, and 42.8%, respectively. The advantage of mAP is the most obvious, with the mAP50 of DAPONet being 8.4%, 5.6%, 9.3%, and 10.4% higher compared to YOLO v5n, YOLOv8n, YOLOv9t, and YOLOv10n, respectively, and the mAP50-95 being 7%, 5%, 6.7%, and 7% higher, which indicates that DAPONet can maintain a higher level of mAP50 under different IoU thresholds can maintain high detection performance, which gives it a strong advantage in complex, multi-scale damage detection tasks, while maintaining high accuracy, DAPONet has significantly lower complexity than other models, with only 1.6 M parameters, 1.7 G FLOPs, and a 3.7 MB model size; the lowest among all compared models. Its mAP50 metric demonstrates superior performance in detecting road damage, which is critical for applications requiring precise identification of road issues. Additionally, the reduced FLOPs  
Figure 7: Experimental models recognize visual results on the SVRDD dataset. and small model size enable real-time processing on devices with limited computational resources, such as mobile phones, edge devices, and on-site inspection tools. These metrics not only showcase DAPONet's enhanced detection capabilities, but also offer practical advantages for real-world deployment. High detection accuracy enhances reliability in applications like autonomous vehicles and mobile-based inspections, while the computational efficiency ensures the model can function effectively in resource-constrained environments.