## 1 Introduction
This can be a significant limitation, especially in scenarios where data is scarce or the target domain differs substantially from the source domain. GAN-based methods can generate  
Figure 1: Pavement distress images under different scenes. (**a**) Vehicle-mounted cameras. (**b**) Dashcams. (**c**) Smartphones. (**d**) Roadside monitoring. (**e**) Drones. (**f**) Motorbikes. synthetic data to augment training sets, but they often struggle with mode collapse and require careful tuning to produce realistic and diverse samples. Additionally, these methods typically focus on improving the model's ability to generalize to new data distributions during training, but they may not be as effective in adapting to dynamic changes in the environment during testing. This limitation can hinder the deployment of these models in real-world applications where conditions can change rapidly. Unlike traditional approaches, test-time adaptation (TTA) offers a more flexible and efficient solution for cross-scenario adaptation. By dynamically adjusting model parameters or structure during the testing phase, TTA can adapt to new data distributions without requiring extensive re-training or additional labeled data (as shown in Figure 2). This makes TTA particularly well-suited for scenarios like pavement distress detection, where data is limited and conditions can vary widely. TTA's ability to adapt in real-time to changing environments and its independence from labeled data make it a promising approach for enhancing the generalization and performance of deep learning models in cross-scenario applications. [PERSON] et al. [24] proposed a test-time domain adaptation framework for monocular depth estimation, which aligned the input features between source and target data by the scale alignment scheme, correcting the absolute scale inference on the target domain. [PERSON] [25] evaluated a holistic test-time adaptation framework for multiple object tracking on a variety of domain shifts, including simulations to real, outdoor to indoor, and indoor to outdoor transitions. Experimental results demonstrate significant improvements in all metrics of the original model. Evidently, TTA shows great performance enhancement in scenes with significant changes in lighting, backgrounds, or other visual conditions. This flexibility and immediacy make TTA an effective solution for cross-source pavement distress detection [26]. Therefore, this study proposes a TTA-based enhancement framework for cross-scene pavement distress detection. The framework first introduces a domain prior knowledge generator to enable unsupervised feature transfer for images from new scenes. Building on this, three key modules--viewpoint conversion, foreground focus, and adaptive normalization--are developed to address the challenges posed by background variations, viewpoint discrepancies, and distribution shifts across different scenes. These modules are integrated into an unsupervised TTA-based cross-scene detection enhancement framework, designed to be seamlessly integrated with any existing pavement detection model. With this proposed framework, existing distress detection models can be dynamically adapted during the prediction process, eliminating the need for retrain  
Figure 2: Transfer learning versus test-time adaptation. data. The effectiveness of each module, as well as the entire framework, has been validated through ablation and effectiveness experiments. By employing unsupervised methods to mitigate domain bias, this framework offers a robust solution to the challenges associated with cross-scene pavement distress detection. The remainder of this paper is organized as follows. Section 2 presents the details of the proposed framework and modules. The dataset used in this study is introduced in Section 3. Section 4 illustrates the results of three experiments. Finally, Section 5 offers a summary of this research.