基于视觉显著性的SAR遥感图像NanoDet舰船检测方法

在合成孔径雷达遥感图像中，舰船由金属材质构成，后向散射强；海面平滑，后向散射弱，因此舰船是海面背景下的视觉显著目标。然而，SAR遥感影像幅宽大、海面背景复杂，且不同舰船目标特征差异大，导致舰船快速准确检测困难。为此，该文提出一种基于视觉显著性的SAR遥感图像NanoDet舰船检测方法。该方法首先通过自动聚类算法划分图像样本为不同场景类别；其次，针对不同场景下的图像进行差异化的显著性检测；最后，使用优化后的轻量化网络模型NanoDet对加入显著性图的训练样本进行特征学习，使系统模型能够实现快速和高精确度的舰船检测效果。该方法对SAR图像应用实时性具有一定的帮助，且其轻量化模型利于未来实现硬件移植。该文利用公开数据集SSDD和AIR-SARship-2.0进行实验验证，体现了该算法的有效性。 

SAR Remote Sensing Image Ship Detection Method NanoDet Based on Visual Saliency

In the Synthetic Aperture Radar (SAR) remote sensing image, ships are visually significant targets on the sea surface. Because they are made of metal, thus the backscatter is strong, while the sea surface is smooth and the backscatter is weak. However, the large-width SAR remote sensing image has a complicated sea background, and the features of various ship targets are quite different. To solve this problem, a SAR remote sensing image ship detection model called NanoDet is proposed. NanoDet is based on visual saliency. First, the image samples are divided into various scene categories using an automatic clustering algorithm. Second, differentiated saliency detection is performed for images in various scenes. Finally, the optimized lightweight network model, NanoDet, is used to perform feature learning on the training samples added with the saliency maps, so that the system model can achieve fast and high-precision ship detection effects. This method is helpful for the real-time application of SAR images. The lightweight model is conducive to hardware transplantation in the future.This study conducts experiments based on the public data set SSDD and AIR-SARship-2.0, and the experiments results verify the effectiveness of our approach.