结合特征融合和注意力机制的SAR舰船检测算法

SAR ship detection algorithm combining feature fusion and attention mechanism

针对现有的合成孔径雷达目标检测算法仅利用图像底层特征进行检测存在的对小尺度舰船目标的检测率较低问题,提出一种结合特征融合和注意力机制的目标检测算法。面向SAR舰船目标检测,在原始主干网络SSD目标检测算法的基础上,引入注意力机制模块、不同层次的特征图进行特征融合、对含有小尺度目标的图像进行过采样还通过多次复制粘贴小目标实现数据增广。实验通过对SAR舰船图像数据集的大量训练和测试,结果表明本文算法能有效提升对舰船目标的综合检测性能,在公开SAR舰船目标检测数据集上平均精度可以达到94.16%。

In order to solve the problem that the existing SAR target detection algorithm only uses the underlying features of the image for detection,and the detection rate of small-scale ship targets is low,an object detection algorithm combining feature fusion and attention mechanism was proposed.For SAR ship-target detection,on the basis of the original backbone network SSD target detection algorithm,the attention mechanism module is introduced,the feature maps at different levels are fused with features,the images containing small-scale targets are oversampled,and the data augmentation is achieved by copying and pasting small targets multiple times.Through a large number of training and testing of SAR ship image datasets,the results show that the proposed algorithm can effectively improve the comprehensive detection performance of ship targets,and the mean average precision can reach 94.16%on the public SAR ship target detection dataset.