E-WFF Net: An Efficient Remote Sensing Ship Detection Method Based on Weighted Fusion of Ship Features

Ships are the main carriers of maritime transportation. Real-time object detection of ships through remote sensing satellites is of great significance in ocean rescue, maritime traffic, border management, etc. In remote sensing ship detection, the complexity and diversity of ship shapes, along with scenarios involving ship aggregation, often lead to false negatives and false positives. The diversity of ship shapes can cause detection algorithms to fail in accurately identifying different types of ships. In cases where ships are clustered together, the detection algorithm may mistakenly classify multiple ships as a single target or miss ships that are partially obscured. These factors can affect the accuracy and robustness of the detection, increasing the challenges in remote sensing ship detection. In view of this, we propose a remote sensing ship detection method, E-WFF Net, based on YOLOv8s. Specifically, we introduced a data enhancement method based on elliptical rotating boxes, which increases the sample diversity in the network training stage. We also designed a dynamic attention mechanism feature fusion module (DAT) to make the network pay more attention to ship characteristics. In order to improve the speed of network inference, we designed a residual weighted feature fusion method; by adding a feature extraction branch while simplifying the network layers, the inference speed of the network was accelerated. We evaluated our method on the HRSC2016 and DIOR datasets, and the results show some improvements compared to YOLOv8 and YOLOv10, especially on the HRSC2016 dataset. The results show that our method E-WFF Net achieves a detection accuracy of 96.1% on the HRSC2016 dataset, which is a 1% improvement over YOLOv8s and a 1.1% improvement over YOLOv10n. The detection speed is 175.90 FPS, which is a 3.2% improvement over YOLOv8 and a 9.9% improvement over YOLOv10n.