Target detection plays an essential role in automatic ship identification, ocean border monitoring, and fishing ships supervision, as well as providing environmental perception capability for booming unmanned marine vehicles. However, the maritime setting poses significant challenges, with frequent occurrences of adverse weather conditions like thick fog and dense mist. These harsh environmental factors can significantly impede ship detection efforts, as vessel images may suffer from diminished visual clarity in real-world scenarios. Prior research has primarily relied on image pre-processing methods, such as defogging and low-light enhancement, to enhance image quality before conducting target detection. However, the additional image pre-processing is time-consuming and would hinder us from realizing fast and efficient ship detection, particularly in adverse environmental conditions. To enhance the real-time ship image detection performance under fog conditions, a deformable attention mechanism is proposed to pay attention to relevant locations with more flexibility, which is immune to time-consuming image processing. Firstly, the publicly available new dataset SeaShips was aggregated by using a synthetic central fog algorithm, which can provide realistic visibility conditions for ship detection. Secondly, a deformable attention mechanism was used in the YOLOv8n model to focus on relevant locations more flexibly. Finally, the mechanism was validated based on the new dataset SeaShips_fog. The experimental findings illustrate that the suggested method attains outstanding detection performance even under foggy atmospheric conditions.