An anchor-free rotated box remote sensing image ship object detection algorithm

Accurate and efficient ship detection plays a crucial role in safeguarding maritime interests and building a maritime powerhouse, with significant practical value. However, anchor boxes are the mainstay of current ship detection algorithms based on optical remote sensing images. These algorithms have limited generalization capabilities, a high computational resource requirement, and a huge number of hyperparameters. Although natural image object detection algorithms have improved these issues by adopting anchor-free methods, they can only achieve horizontal box detection and are unable to handle the unique characteristics of ship targets, such as elongated shapes, varying angles, and tight arrangements. To address these issues, this paper designs an anchor-free, single-stage remote sensing image ship detection algorithm. In particular, based on CenterNet, this research suggests a distribution-prior based confidence coefficient prediction branch to produce higher quality positive samples for the ship detection job. More precise rotation angle representation is achieved by limiting the output space of angles using a hyperbolic activation-based angle prediction branch. The use of a variable positive-negative sample label assignment technique can speed up network convergence by providing dynamic, fine-tuned supervision information. Experiments on the HRSC2016 dataset validate the superiority of the proposed algorithm compared to other advanced algorithms and confirm the effectiveness of each module.