## 4 Results and Analysis
### Comparative Experiment  
To validate the superiority of the model proposed in this study, comparative experiments were conducted with methods such as CycleGAN [31], EasyTL [32], DDTCDR [33], and GraftNet [34]. Figure 10 presents the visual results of the comparative experiments. When using the CycleGAN algorithm, images from the test dataset are stylized to resemble the style of the training data, indirectly adapting the model to the distribution of the test data. However, this approach struggles to address differences in viewpoint between the test and training data. The EasyTL method, on the other hand, aims to align the distribution of test data with that of the training data, helping the model better adapt to the test domain. DDTCDR (Domain Distribution Transfer for Cross-Domain Recognition) improves the model's generalization ability by considering the proximity of the distributions between the training and test datasets, effectively bridging the gap between them. GraftNet enhances model performance on test data by pre-training to extract common features shared between the training and test data. It then customizes a divergent branch based on the unique distribution characteristics of the test data. However, most of the above methods mainly focus on the distribution differences between the test and train datasets, without adequately addressing differences in viewpoint and background. Consequently, their ability to improve the recognition capability of pavement distress detection models is limited. In contrast, the method proposed in this study comprehensively considers differences in three aspects and proposes targeted solutions. Its detection results are more accurate, further demonstrating the superiority of the proposed method. This study quantitatively analyzed the impact of various methods on the recognition accuracy of pavement distress detection, as shown in Table 6. From the table, it is evident that the method proposed in this study outperforms other methods in various indicators of pavement distress detection. In particular, the DDTCDR and GraftNet methods exhibit detection accuracy even lower than that of the original YOLO and FasterRCNN models. This is primarily because these methods, when transferring the pavement distress detection model to the target domain, fail to effectively promote the model's adaptation to the target  
Figure 10: Detection result of comparative experiments. domain's distribution. Instead, they interfere with the model's recognition of target domain images, leading to a decrease in accuracy. This paper also calculated the runtime of various methods. The TTA-YOLO model in this study consumes 0.023 s to recognize an image, and the TTA-FasterRCNN model takes up to 0.43 s, which is the longest among all models. However, the slow processing speed of TTA-FasterRCNN is mainly attributed to the inherently slow recognition speed of the FasterRCNN model itself. As for TTA-YOLO, the processing speed of 0.023 s per frame still ensures that the pavement distress detection model is real-time and can meet practical engineering requirements. Furthermore, in terms of model accuracy, the proposed framework in this paper far exceeds other models, thereby demonstrating the superiority of the proposed method. On the other hand, TTA models do not require additional annotated data from the target domain and can achieve performance close to models obtained through transfer learning. As shown in Table 6, transfer learning models can achieve the best detection performance by using annotated data from the target domain for training. This is mainly because the pavement distress detection model trained in the source domain has already learned some features of pavement distress. When transfer learning is applied in the target domain, the model further learns the specific features of pavement distress in the target domain and adapts to its characteristics, such as distress background, image lighting intensity, and shooting angles.