When deep-learning-based target detection algorithms are directly applied to the complex scene images generated by space optical remote sensing （SORS）， the ship target detection effect is often poor. To address this problem， this paper proposes an improved YOLOX-S （IM-YOLO-s） algorithm， which uses densely arranged offshore ships with complex backgrounds and ships with multi-interference and small targets in the open sea as detection objects. In the feature extraction stage， the CA location attention module is introduced to distribute the weight of the target information along the height and width directions， and this improves the detection accuracy of the model. In the feature fusion stage， the BiFPN weighted feature fusion algorithm is applied to the neck structure of IM-YOLO-s， which further improves the detection accuracy of small target ships. In the training stage of model optimization， the CIoU loss is used to replace the IoU loss， zoom loss is used to replace the confidence loss， and weight of the category loss is adjusted， which increases the training weight in the densely distributed areas of positive samples and reduces the missed detection rate of densely distributed ships. In addition， based on the HRSC2016 dataset， additional images of small and medium-sized offshore ships are added， and the HRSC2016-Gg dataset is constructed. The HRSC2016-Gg dataset enhances the robustness of marine ship and small and medium-sized pixel ship detection. The performance of the algorithm is evaluated based on the dataset HRSC2016-Gg. The experimental results indicate that the recall rate of IM-YOLO-s for ship detection in the SORS scene is 97.18%， AP@0.5 is 96.77%， and the F1 value is 0.95. These values are 2.23%， 2.40%， and 0.01 higher than those of the original YOLOX-s algorithm， respectively. This indicates that the algorithm can achieve high quality ship detection from SORS complex background images.