LAUSR

Ship detection is a significant and challenging task in remote sensing. At present, anchor-based ship detectors have achieved remarkable results, but they require introducing additional parameters and their performance is easily affected by the size of anchor boxes. In this letter, we propose an anchor-free rotated detector (OFCOS) for ship detection based on fully convolutional one-stage detector (FCOS), which can be trained end-to-end. Specifically, a feature-enhanced feature pyramid network (FE-FPN) is proposed, in which the structure of the feature pyramid is optimized and an attention mechanism is introduced during fusion to enhance the significance of object features. Then, to better describe the orientation of objects, a regression branch with orientation characterization capability is constructed, and a center-to-corner bounding box prediction strategy is used to improve the accuracy of object localization. Moreover, the calculation of centerness is optimized so that the assignment of center weights is orientation-aware and adaptive to downweight low-quality predictions. A new remote sensing ship dataset, named RS-Ship, is constructed to further verify the effectiveness and robustness of OFCOS. Our experiments show that OFCOS achieves AP values of 91.07% and 97.05% on the publicly available dataset HRSC2016 and our self-built RS-Ship dataset, respectively, which are 13.01% and 9.84% higher than FCOS. OFCOS outperforms other mainstream detection methods in terms of both detection speed and detection accuracy.