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SAR Ship Target Detection for SSDv2 under Complex Backgrounds

机译：在复杂背景下SSDv2的SAR船舶目标检测

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With the launch of space-borne satellites, more synthetic aperture radar (SAR) images are available than ever before, thus making dynamic ship monitoring possible. Object detectors in deep learning achieve top performance, for example, Faster R-CNN, ResNet, FPN, YoLo and SSD Net. Compared with YoLo, SSD algorithm performs better in accuracy and speed, but it is not good at small targets detection like SAR images. To solve this problem, we proposed an improved method. We added a deconvolution module and prediction module on the basis of SSD. The deconvolution module is mainly used to integrate the high-level semantic information into the feature information of the low-level network so as to improve the detection accuracy. The prediction module, which is composed of residual network, can extract depth features and input them into regression task and classification task., we call it SSDv2. We test our model in the SAR-Ship-Dataset. SAR-Ship-Dataset was created using 102 Chinese Gaofen-3 images and 108 Sentinel-1 images. It consists of 43,819 ship chips of 512 pixels in both range and azimuth. These ships mainly have distinct scales and backgrounds. The result shows that our SSDv2-512*512 achieves 91.07% mAP, outperforming a state-of-the-art method SSD.
机译：随着空间卫星的推出，更多的合成孔径雷达（SAR）图像可获得比以前可用，从而使动态船舶监控成为可能。深度学习中的对象探测器实现顶部性能，例如，更快的R-CNN，Reset，FPN，YOLO和SSD网。与yolo相比，SSD算法的精度和速度更好，但在SAR图像等小目标检测时不擅长。为了解决这个问题，我们提出了一种改进的方法。我们在SSD的基础上添加了解卷积模块和预测模块。解卷积模块主要用于将高电平语义信息集成到低级网络的特征信息中，以提高检测精度。由剩余网络组成的预测模块可以提取深度特征并将其输入回归任务和分类任务。，我们称之为SSDv2。我们在SAR-Ship-DataSet中测试我们的模型。 SAR-Ship-DataSet使用102中文GaoFen-3图像和108 Sentinel-1图像创建。它包括在范围和方位角的43,819艘船上的512像素。这些船主要具有不同的尺度和背景。结果表明，我们的SSDV2-512 * 512达到了91.07％的地图，优于最先进的方法SSD。

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