针对合成孔径雷达(synthetic aperture radar, SAR)图像近岸船舶目标受背景杂波的影响，导致SAR图像船舶目标检测检测率低和小尺度舰船目标©检率高的问题，提出了一种适用于复杂背景下SAR 图像近岸舰船目标检测的改进FPN（Feature Pyramid Network）模型。该模型基于FPN目标检测算法的基础上，首先在特征提取网络中利用可变形卷积更加精确的确定对目标采样点λ置，增强目标的特征提取能力，提高复杂背景下SAR 图像舰船标的检测率；同时采用通道注意力机制来捕获特征提取网络中不同通道图之间的特征依赖关系，降低©检率。在公开的SAR图像舰船数据集上测试实验，结果表明本文提供的模型在复杂场景下的检测精度为87.95%，相比于原始的FPN提升了8.46%。其中针对小尺度舰船目标检测精度为95.14%，相比于原始的FPN检测精度提升了5.28%。对比Yolo5和mask RCNN，融合了上述方法的改进模型平均检测准确精度分别提升了11.21%，2.98% 。

Aiming at the problem that the nearshore ship targets in synthetic aperture radar (SAR) images were affected by the background clutter, that resulted in lower detection rate of ship targets in SAR images and higher false alarm rate and missed detection rate of smallscale ship targets, an improved feature pyramid network (FPN) model for nearshore ship target detection in SAR images under complex backgrounds was proposed based on the FPN target detection algorithm. The deformable convolution was used in the feature extraction network to determine the target sampling point position more accurately to enhance the target feature extraction ability and improve the detection rate of ship targets in SAR images under complex backgrounds. At the same time, the channel attention mechanism was used to capture the feature dependencies relationship between different channel graphs in the feature extraction network and reduce the missed detection rate. Test experiments on the public SAR image ship dataset show that the detection accuracy of the model in complex scenes is 87.95%, which is 8.46% higher than the original FPN. There in, the detection accuracy for smallscale ship targets is 95.14%,which is 5.28% higher than the original FPN. Compared with Yolo5 and mask RCNN, the average detection accuracy of the improved FPN model increases by 11.21% and 2.98% respectively.