基于感知向量的光学遥感图像舰船检测

针对光学遥感图像中近岸舰船目标检测干扰大、虚警率高的问题, 在基于包围框边缘感知向量(box boundary-aware vectors, BBAVectors)检测网络的基础上提出了改进方法。首先在特征融合网络后加入一个有监督的注意力模块来增强目标区域信息, 削弱无关背景信息干扰; 然后利用边界感知向量间的几何关系设计了一个自监督损失函数, 用以加强向量间的耦合关系, 防止向量独立性导致包围框出现不规则形状。实验结果显示, 在HRSC2016数据集L₂级检测任务中, 改进模型检测结果的平均精度相较于原网络提高了6.91%, 有效抑制了背景噪声的干扰, 降低了近岸舰船目标检测的虚警率, 证明了改进方法的有效性。

Ship detection of optical remote sensing images based on aware vectors

To solve the problem of severe interference and high false positive rate in ship detection from remote sensing images, an enhanced method based on the box boundary-aware vectors (BBAVectors) detection network is proposed.Firstly, a supervised attention module is added to the feature fusion network to enhance the relevant information within the target region and reduce the interference of irrelevant background information. Then a self-supervised loss function is proposed based on the geometric relations among the boundary vectors to guarantee the coupling relation between vectors and prevent the irregular shape of the bounding boxes caused by vectors' independence. Experimental results in the L₂ level detection task on the HRSC2016 dataset show that the mean average precision of the detection results for the proposed model gets improved by 6.91% compared with the original network. The proposed model can effectively suppress the interference of background noise and reduce the false alarm rates in near-shore ship detection, which demonstrates its effectiveness.