RT-FogNet: real-time ship detection under low-visibility conditions in inland waterways.

Real-time ship detection in inland waterways is critically challenged by low-visibility conditions such as dense fog, backlight, and surface reflections. To address this, we propose RT-FogNet, a lightweight and deployable detection framework tailored for practical inland waterway scenarios. RT-FogNet integrates a Water Surface Image Dehazing (WSID) module to improve image clarity through a domain-adaptive compression and alignment strategy, and builds upon an enhanced YOLOv10-based detection backbone, into which a Dilated Shared Spatial Pyramid Pooling Fast (DS-SPPF) module is incorporated to strengthen multi-scale feature representation with minimal computational overhead. To support robust evaluation under diverse weather conditions, we introduce the Inland Waterway Ship Dataset (IWSD), comprising 9,305 annotated images collected from real-world inland scenes. Experiments conducted on COCO, SeaShips, and IWSD datasets demonstrate that RT-FogNet achieves significant performance gains, including over 15 percentage points improvement in AP on IWSD, while maintaining high inference speed and low parameter complexity. These results confirm the practical suitability of RT-FogNet for real-time deployment in vision-based ship monitoring systems. The dataset and code are publicly available at https://github.com/Object-Detection-01/RT-FogNet .