FCOSR:一种无锚框的SAR图像任意朝向船舶目标检测网络

以FCOS为代表的无锚框网络避免了预设锚框带来的超参设定问题，然而其水平框的输出结果无法指示任意朝向下SAR船舶目标的精确边界和朝向。针对此问题，该文提出了一种名为FCOSR的检测算法。首先在FCOS回归分支中添加角度参量使其输出旋转框结果。其次，引入基于可形变卷积的9点特征参与船舶置信度和边界框残差值的预测来降低陆地虚警并提升边界框回归精度。最后，在训练阶段使用旋转自适应样本选择策略为每个船舶样本分配合适的正样本点，实现网络检测精度的提高。相较于FCOS以及目前已公开发表的锚框旋转检测网络，该网络在SSDD+和HRSID数据集上表现出更快的检测速率和更高的检测精度，mAP分别为91.7%和84.3%，影像切片平均检测时间仅需33 ms。 

FCOSR: An Anchor-free Method for Arbitrary-oriented Ship Detection in SAR Images

The anchor-free network represented by a Fully Convolutional One-Stage object detector (FCOS) avoids the hyperparameter setting issue caused by the preset anchor boxes; however, the result of the horizontal bounding boxes cannot indicate the precise boundary and orientation of the arbitrary-oriented ship detection in synthetic-aperture radar images. To solve this problem, this paper proposes a detection algorithm named FCOSR. First, the angle parameter is added to the FCOS regression branch to output the rotatable bounding boxes. Second, 9-point features based on deformable convolution are introduced to predict the ship confidence and the boundary-box residual to reduce the land false alarm and improve the accuracy of the boundary box regression. Finally, in the training stage, the rotatable adaptive sample selection strategy is used to allocate appropriate positive sample points to the real ship to improve the network detection accuracy. Compared to the FCOS and currently published anchor-based rotatable detection networks, the proposed network exhibited faster detection speed and higher detection accuracy on the SSDD+ and HRSID datasets with the mAPs of 91.7% and 84.3%, respectively. The average detection time of image slices was only 33 ms.