针对目前基于深度学习的舰船目标斜框检测方法存在计算量大、效率低的问题，提出一种基于目标中心点的单阶段检测模型.由于舰船中心点不受舰船分布方向影响，模型主要思想是以目标中心点检测为基础，回归中心点处目标斜框的尺度和方向.首先设计特征提取网络，将卷积神经网络细节信息丰富的底层特征与语义信息丰富的高层特征融合起来形成特征图；然后将特征图输入到三个检测分支，分别预测目标中心点、中心点偏移值以及斜框的尺度与方向；设计组合损失函数对网络进行训练，并改进非极大值抑制算法以适应目标斜框检测的需要.在公开的SAR图像舰船目标检测数据集与光学遥感图像上进行了实验，实验结果表明，测试集平均准确率达0.906，检测精度与速度均优于其它检测模型，充分验证了所提算法的有效性.

The recent proposed deep learning-based arbitrary-oriented objects detection algorithms increase extra computation burden and could not work efficiently. A one-stage model based on object centers detection is proposed for arbitrary-oriented ship detection. As the centers of objects are free from the influence their distribution directions, the key of the model is to regress the parameters of object's oriented bounding box on the basis of center detection. Firstly, a feature extracting network is designed to achieve feature map and a new feature fusion method is proposed which aggregates the low-level features rich in detailing information and high-level features rich in semantic information together. Then the feature map is entered to three detection branches, which predict of centers, offsets of centers, and size and direction of the oriented bounding boxes respectively. A combined loss function is proposed for the training of the network, and a modified non-maximum suppression algorithm is proposed for removing invalid oriented bounding boxes. The proposed model achieves state-of-art performance in public SAR ship detection dataset with mean average precision as 0.906, outstanding than other methods both in speed and precision.