Abstract: Remote sensing image ship target detection has been extensive used in engineering. However, there are currently few studies on fine-grained detection tasks and a lot of research on coarse-grained detection techniques. Fine-grained detection in remote sensing ship photos has two types of difficulties：the first is that the target is dispersed at any angle, and the second is that coarse and fine-grained characteristics of the target are intermingled. Due to these difficulties, current fine-grained detection techniques have poor detection accuracy. This research proposes an enhanced Oriented R-CNN network-based fine-grained identification approach for remote sensing ship image rotating targets in order to address the aforementioned issues. Specifically addressing the issue of ship targets that can be found in any angle in remote sensing images, this research presents a novel approach to produce candidate boxes for rotating targets based on Oriented R-CNN network, which may efficiently remove background redundant information and enhance model performance. In order to properly retain the coarse and fine-grained characteristics of targets and increase the power of fine-grained detection, a feature refusion approach is presented in this study to incorporate FPN layers and multilevel features in the two-stage network feature mapping stage. The experimental results demonstrate that the modified model has a strong detection effect and a detection accuracy of 83.57% on HRSC2016, a publicly accessible fine-grained detection dataset for remote sensing ships.

Key words: fine-grained detection, remote sensing image, rotation detection, ship target

摘要： 遥感图像舰船目标检测技术在工程上得到广泛应用。但是，目前的研究主要集中在粗粒度检测方法上，面向细粒度检测任务的研究较少。在遥感舰船图像中，细粒度检测面临两类挑战：一是目标任意角度分布，二是目标粗细粒度特征混杂，这些挑战造成现有细粒度检测方法存在检测精度不高的问题。为了解决上述问题，提出基于改进Oriented R-CNN网络的遥感舰船图像旋转目标细粒度检测方法。针对遥感图像中舰船目标任意角度分布的问题，基于Oriented R-CNN网络，提供了新的旋转目标候选框生成方法，有效降低背景冗余信息，提升模型表现。针对粗细粒度混杂的问题，提出特征再融合方法，在二阶段网络特征映射阶段融合FPN多层特征，充分保留目标的粗细粒度特征，提升细粒度检测能力。实验结果显示，改进模型在公开的遥感舰船细粒度检测数据集HRSC2016上，检测精度达到83.57%，取得了较好的检测效果。

关键词: 细粒度检测, 遥感图像, 旋转检测, 舰船目标