In this study, we propose an end-to-end detection model based on cascaded spatial priors and dynamic upsampling for ship detection tasks in day–night transition environments, named the Cascaded Group and Dynamic Upsample-DEtection TRansformer (CGDU-DETR). To address the limitations of traditional methods in complex lighting conditions (e.g., strong reflections, low light), we designed a novel CG-Net model based on cascaded group attention and introduced a dynamic feature upsampling algorithm, effectively enhancing the model’s ability to extract multi-scale features and detect targets in complex backgrounds. The experimental results show that the CGDU-DETR achieves an AP of 93.4% on the day–night transition dataset, representing a 2.86% improvement over YOLOv12, and a recall of 95.2%, representing a 24.44% improvement over YOLOv12. Particularly for complex categories such as cargo ships and law enforcement vessels, the CGDU-DETR significantly outperforms YOLOv12, with improvements of 35.9% in AP and 63.7% in recall. Moreover, generalization experiments on the WSODD public dataset further validate the robustness of the model, with the CGDU-DETR achieving an AP of 95.1%, representing an 11.6% improvement over YOLOv12. These results demonstrate that the CGDU-DETR has significant advantages in ship detection tasks under day–night transition environments, effectively handling complex lighting and background interference, and providing reliable technical support for all-weather maritime surveillance.