ABSTRACT

In view of characteristics of the ship in the optical remote-sensing image, such as multiple dimensions, majority of small objects, crowded arrangement and complex background, and so on, the paper presents a ship detection framework combining the network-fusing multi-level features crossing levels, the rotation region proposal network and the bidirectional recurrent neural network fusing self-attention mechanism. Firstly, we set up a network fusing multi-level features crossing levels because of the multiple scales and diverse characteristics of the remote-sensing ships to increase the precision of feature extraction of the ship, thus improving the performance in the multiple scales, small objects, and complex background problems. Secondly, we separately design the ROI Pooling Layer and the bidirectional recurrent neural network fusing self-attention mechanism, which infuses the prior information of ship dimension and position to realize a good performance and precise ship positioning in crowded scenes. Finally, we verify the effectiveness of the proposed method through experiments, the experimental dataset includes the private dataset designed by us based on Google Earth, the ship dataset in DOTA-Ship and HRSC2016 public ship dataset. The results verify the contributions of each proposed module, and the comparison results show that our proposed method has a state-of-the-art performance.

摘要

针对光学遥感影像中船舶的多维度、小物体多、排列拥挤、背景复杂等特点，提出了一种结合多层次网络融合的船舶检测框架。具有交叉级别、旋转区域提议网络和融合自注意力机制的双向循环神经网络。首先，针对遥感舰船的多尺度、多样性特点，建立跨层级多级特征融合网络，提高舰船特征提取的精度，从而提高在多尺度、小目标下的性能。 ，以及复杂的背景问题。第二，我们分别设计了 ROI Pooling Layer 和双向循环神经网络融合自注意力机制，注入船舶尺寸和位置的先验信息，在拥挤的场景中实现良好的性能和精确的船舶定位。最后，我们通过实验验证了所提出方法的有效性，实验数据集包括我们基于Google Earth设计的私有数据集、DOTA-Ship中的船舶数据集和HRSC2016公共船舶数据集。结果验证了每个提出的模块的贡献，比较结果表明我们提出的方法具有最先进的性能。我们通过实验验证了所提出方法的有效性，实验数据集包括我们基于Google Earth设计的私有数据集、DOTA-Ship中的船舶数据集和HRSC2016公共船舶数据集。结果验证了每个提出的模块的贡献，比较结果表明我们提出的方法具有最先进的性能。我们通过实验验证了所提出方法的有效性，实验数据集包括我们基于Google Earth设计的私有数据集、DOTA-Ship中的船舶数据集和HRSC2016公共船舶数据集。结果验证了每个提出的模块的贡献，比较结果表明我们提出的方法具有最先进的性能。