To improve the accuracy of on-orbit object detection in remote-sensing images under complex scenarios, this paper proposes a detection method based on multi-scale feature fusion. First, a multi-scale feature fusion module is designed to expand the model′s receptive field, where parallel multi-channel fusion is employed to extract multidimensional target features. Second, a spatial-channel feature interaction module is constructed by integrating an attention mechanism to enhance the model′s ability to capture detailed features in salient regions. Third, the normalized Wasserstein distance is introduced to improve the loss function, optimizing the measurement of positional offsets between overlapping anchor boxes and enhancing discrimination of densely overlapped targets. In addition, a ship dataset named JS25k is annotated on “Jilin-1” satellite imagery with data augmentation and other preprocessing steps. The proposed model achieves a final detection accuracy of 98.2%, outperforming the latest YOLOv13 by 0.6% and exhibiting better performance than the RT-DETR series. When deployed on an edge-side embedded platform, the detection speed reaches 92.3 fps. Experimental results demonstrate that the model effectively improves detection accuracy in complex scenes while meeting real-time requirements for on-orbit applications.