Effectively obtaining the location and direction of the ship target is an important prerequisite for maritime traffic management and marine accident rescue. Thanks to the rapid development of the target detection methods based on deep learning, this article proposed a ship target detection method for multiresolution synthetic aperture radar (SAR) images based on improved region convolution neural network (R‐CNN). According to the characteristics of ship target in the SAR images, we make several improvements such as enlarging the input, proposal optimization, database target categorization, and weight balance on the basis of the standard Faster R‐CNN. The experimental results proved that the proposed method can detect target effectively and precisely in complicated scenes of multiresolution SAR images, such as in‐shore and dense targets. It has a good potential in practical application.

中文翻译：

---

用于多分辨率合成孔径雷达图像船舶检测的改进区域卷积神经网络

有效获取船舶目标的位置和方向是海上交通管理和海上事故救援的重要前提。由于基于深度学习的目标检测方法的快速发展，本文提出了一种基于改进区域卷积神经网络（R-CNN）的多分辨率合成孔径雷达（SAR）图像船舶目标检测方法。根据SAR图像中船舶目标的特点，我们在标准Faster R-CNN的基础上，进行了输入放大、proposal优化、数据库目标分类、权重平衡等多项改进。实验结果证明，该方法能够有效、准确地检测出多分辨率SAR图像复杂场景中的目标，如近海目标和密集目标。