## 1 Introduction  
Computer vision has made significant strides in pavement distress detection, particularly in vehicle-mounted cameras [1] or smartphones [2] scenarios (as shown in Figure 1a-c). While advanced object detection models have been proposed [3; 4; 5], their performance often degrades when applied to new, unseen scenarios (Figure 1d-f), such as roadside monitoring [6], drones [7], and motorbikes [8]. This cross-scenario generalization gap arises from variations in lighting, weather, road types, and the significant effort required for data annotation and model adaptation. These limitations hinder the widespread deployment of existing models in diverse real-world applications. Numerous studies have investigated enhancing cross-scene detection performance, primarily focusing on data and model aspects. In terms of model, transfer learning is a widely used technique for enhancing cross-scene detection performance by adapting pre-trained models to new domains [9; 10]. [PERSON] et al. [11] proposed a transfer learning method based on multiple pre-trained models to achieve pavement crack segmentation. [PERSON] et al. [12] used the transfer learning method to apply pavement damage detection across scenes to street view images, requiring over 20,000 labeled data samples for fine-tuning. Similarly, [PERSON] et al. [13] created their own dataset with 18,700 labeled images and implemented the transfer application of pavement distress detection based on vehicle-mounted motion cameras. Relying on 26,620 annotated datasets, [PERSON] et al. [14] explored the performance of transfer learning algorithms in multi-country cross-scenario detection tasks. [PERSON] et al. [15] utilized transfer learning to recognize pavement distresses from images sourced from three different datasets, achieving high detection accuracy across multiple distress categories. However, the model struggled when transitioning from perpendicular to parallel camera angles, often misclassifying roadside vehicles as pavement distresses. It can be seen that transfer learning methods still face significant challenges, including negative transfer, domain shift, and the requirement for substantial labeled data [16]. Negative transfer occurs when the source domain's knowledge is misapplied to the target domain, leading to performance degradation. Domain shift, a mismatch in data distribution between domains, can hinder effective knowledge transfer, even in highly related scenarios. Moreover, the need for extensive labeled data for fine-tuning limits the rapid deployment of models in new environments. To address data scarcity in new scenarios, generative adversarial networks (GANs) have been employed to synthesize realistic pavement images. By learning the distribution of training data, GANs can generate diverse images with varying lighting, weather, and viewpoint conditions. This approach has shown promise in improving detection accuracy, particularly for tasks like cracks [17; 18; 19] and potholes [20; 21; 22] detection. [PERSON] et al. [23] proposed a lightweight GAN structure for automatic recognition of pavement distress, which enhances computational efficiency and reduces costs by reducing the parameters of the model layer. However, GANs are computationally expensive to train and can produce artifacts in generated images, which may negatively impact model performance. Additionally, the limited diversity of generated data can hinder the model's generalization ability to real-world scenarios. Thus, there remains a strong demand for efficient and effective methods to enhance cross-scenario detection. In summary, current methods for improving cross-scenario generalization primarily rely on training-phase techniques, such as transfer learning and GAN-based data augmentation. Transfer learning can leverage knowledge from a source domain to a target domain, but it heavily relies on the availability of sufficient labeled data in the target domain.