A Multi-Scale Attention Fusion Network and Cosine Similar Loss for SAR Ship Detection
Deep learning algorithms are widely used in the field of synthetic aperture radar(SAR)image ship de-tection for their advantages of end-to-end training and high accuracy.However,ship targets in SAR images span a large size and are susceptible to the interference from complex backgrounds and noise,which affects the detection accuracy.To further improve the detection accuracy of the network,a multi-scale attention fusion network(MAF-Net)is proposed in this paper.The network mainly contains a multi-scale feature attention fusion(MFAF)module,which uses the fea-ture maps output from the backbone network,fuses the multi-scale information,and enhances the feature maps output from the FPN in the spatial and channel dimensions.In this way,the influence of noise and background on the ship tar-get is suppressed and the feature extraction capability of the network is enhanced.In addition,a cosine similar(CS)loss is proposed,which enables the network to more accurately distinguish the ship target from the background by calcu-lating the cosine similarity between the target and non-target regions,to further improve the accuracy.Numerous experi-ments show that the proposed methods have higher detection accuracy compared with several existing algorithms on SS-DD and SAR-Ship-Dataset datasets.
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