With the rapid expansion of China’s high-speed rail network, ensuring the safety of rail infrastructure through precise and efficient inspection technologies has become paramount. Unmanned Aerial Vehicle (UAV) inspections have emerged as a promising alternative to manual methods. However, accurately and efficiently identifying defects within UAV-captured images remains a challenge. This is largely due to the presence of small targets, limited defect samples, complex backgrounds, and cross-domain variations in high-speed rail infrastructure images. Conventional detection algorithms struggle to address these challenges effectively. To tackle the problem of cross-domain few-shot defect detection, we propose a method that integrates a multiscale attention mechanism with incremental data augmentation. The multiscale attention module enhances the detection of small targets, improving the model’s sensitivity to both channel and positional information. This leads to higher accuracy in target localization and reduces the occurrence of false positives and missed detections. Additionally, the progressive data augmentation technique helps mitigate feature distribution discrepancies between the source and target domains. The method in this paper demonstrates superior performance in few-shot detection scenarios, particularly in 1-shot, 2-shots, 3-shots, and 5-shots settings, where it achieves mAP50 scores of 19.1%, 20.9%, 19.7%, and 21.0%, respectively, outperforming other baseline models. These results indicate a significant improvement in cross-domain few-shot defect detection for high-speed rail infrastructure, highlighting the efficacy of our approach.