论文标题
上下文保护实例级增强和用于SAR船检测的可变形卷积网络
Context-Preserving Instance-Level Augmentation and Deformable Convolution Networks for SAR Ship Detection
论文作者
论文摘要
由于随机取向和雷达信号阻塞引起的部分信息丢失，SAR图像中目标的形状变形是SAR船检测的必要挑战。在本文中，我们提出了一种数据增强方法，以训练一个深层网络，该网络对目标内的部分信息丢失具有牢固的态度。利用对边界框和实例细分面膜的地面真相注释，我们提出了一个简单有效的管道，以模拟实例级别中目标的信息丢失，同时保留上下文信息。此外，我们采用可变形的卷积网络来适应从几何翻译目标中提取形状不变的深度特征。通过学习对标准卷积网格的采样偏移，网络可以从具有SAR船检测的形状变化的目标中鲁棒提取特征。 HRSID数据集的实验，包括与其他深网和增强方法的比较以及消融研究，证明了我们提出的方法的有效性。
Shape deformation of targets in SAR image due to random orientation and partial information loss caused by occlusion of the radar signal, is an essential challenge in SAR ship detection. In this paper, we propose a data augmentation method to train a deep network that is robust to partial information loss within the targets. Taking advantage of ground-truth annotations for bounding box and instance segmentation mask, we present a simple and effective pipeline to simulate information loss on targets in instance-level, while preserving contextual information. Furthermore, we adopt deformable convolutional network to adaptively extract shape-invariant deep features from geometrically translated targets. By learning sampling offset to the grid of standard convolution, the network can robustly extract the features from targets with shape variations for SAR ship detection. Experiments on the HRSID dataset including comparisons with other deep networks and augmentation methods, as well as ablation study, demonstrate the effectiveness of our proposed method.
