ABSTRACT

Ship detection in Synthetic Aperture Radar (SAR) images has always been a hot topic for research. The development of Deep Neural Networks (DNNs) has strongly promoted the development of computer vision. DNNs are also increasingly applied to SAR ship detection. However, SAR ship detection still faces the following problems: (i) The network used for detection needs to be pre-trained on ImageNet, but there is a large bias between SAR images and ImageNet, which leads to training bias. (ii) The sizes of ship targets vary greatly, and many DNNs do not perform well on multi-scale and small-size SAR ship detection. Therefore, we have designed a SAR ship detector that does not require pre-training. We use DetNet as the backbone network, adopting stacked convolution instead of down-sampling to solve the problem of small object detection and adopt a feature reuse strategy to improve parameter efficiency. In addition, we introduce several branches in the proposal sub-network to provide multi-scale object detection. In the detection sub-network, we use position-sensitive region of interest pooling to improve the prediction accuracy. Experiments on SAR ship dataset prove that our method performs better than some pre-trained networks for small ship detection and complex background ship detection.

摘要

合成孔径雷达（SAR）图像中的船舶检测一直是研究的热点。深度神经网络（DNN）的发展极大地促进了计算机视觉的发展。DNN也越来越多地应用于SAR船舶检测。但是，SAR舰船检测仍然面临以下问题：（i）用于检测的网络需要在ImageNet上进行预训练，但是SAR图像和ImageNet之间存在很大的偏差，从而导致训练偏差。（ii）船舶目标的大小相差很大，许多DNN在多尺度和小型SAR船舶检测中表现不佳。因此，我们设计了一种不需要预先训练的SAR船舶探测器。我们使用DetNet作为骨干网，采用堆叠卷积代替下采样来解决小目标检测问题，并采用特征重用策略来提高参数效率。此外，我们在提案子网中引入了几个分支机构，以提供多尺度目标检测。在检测子网中，我们使用位置敏感的兴趣区域池来提高预测精度。在SAR舰船数据集上的实验证明，对于小舰船检测和复杂背景舰船检测，该方法的性能优于某些预训练网络。