【摘要】 针对复杂场景下的多尺度SAR舰船目标检测存在误检漏检的问题，提出了一种改进的SAR舰船目标检测方法。首先，利用多尺度目标特征提取网络提取特征信息，以提升多尺度目标的检测能力并减少冗余计算。其次，引入可形变卷积（DConv）通过自适应调整卷积核的形状来提升复杂场景下SAR舰船目标的检测性能。最后，引入了注意力机制来抑制背景杂波并增强特征信息。实验结果表明，在SSDD数据集和HRSID数据集上改进方法的检测精度分别达到了97.9%和93.1%，整体性能优于现有主流目标检测算法。更多还原

【Abstract】 Aiming at the problem of false detection and missing detection in multi-scale SAR ship object detection in complex scenes, an improved SAR ship object detection method is proposed in this paper. Firstly, a multi-scale object feature extraction network(MFE-Net) is used to extract feature information to improve the detection capability of multi-scale objects and reduce redundant calculations. Secondly, deformable convolution(DConv) is introduced to improve the detection performance of SAR ships in complex scenarios by adjusting the shape of the convolution kernel adaptively. Finally, an attention mechanism is introduced to suppress background clutter and enhance feature information. The experimental results show that the detection accuracy of the proposed method on SSDD and HRSID data sets reaches 97.9% and 93.1%, respectively, and the overall performance is better than the existing mainstream object detection algorithms.更多还原