论文标题
SAR-SHIPNET：通过双向协调关注和多分辨率功能融合的SAR-SHIP检测神经网络
SAR-ShipNet: SAR-Ship Detection Neural Network via Bidirectional Coordinate Attention and Multi-resolution Feature Fusion
论文作者
论文摘要
本文研究了神经网络中合成孔径雷达（SAR）图像实际上有意义的船舶检测问题。我们广泛提取不同类型的SAR图像特征，并提出一个有趣的问题，即这些提取的特征是否有益于（1）抑制现实世界中SAR图像的数据变化（例如，复杂的陆地背景，散射噪声），以及（2）增强船体的特征，这些特征是小物体，并且具有不同的物体并具有不同的面（长度（长度）），从而在改善船只中均可攻击。为了回答这个问题，我们提出了一个基于Centernet的新开发的双向坐标（BCA）和多分辨率特征融合（MRF），提出了一个SAR-Ship检测神经网络（简称为SAR-ShipNet）。此外，考虑到任意船只的长度宽度比，我们采用椭圆形的高斯概率分布，以提高基本探测器模型的性能。公共SAR-SHIP数据集的实验结果表明，我们的SAR-ShipNet在速度和准确性上都具有竞争优势。
This paper studies a practically meaningful ship detection problem from synthetic aperture radar (SAR) images by the neural network. We broadly extract different types of SAR image features and raise the intriguing question that whether these extracted features are beneficial to (1) suppress data variations (e.g., complex land-sea backgrounds, scattered noise) of real-world SAR images, and (2) enhance the features of ships that are small objects and have different aspect (length-width) ratios, therefore resulting in the improvement of ship detection. To answer this question, we propose a SAR-ship detection neural network (call SAR-ShipNet for short), by newly developing Bidirectional Coordinate Attention (BCA) and Multi-resolution Feature Fusion (MRF) based on CenterNet. Moreover, considering the varying length-width ratio of arbitrary ships, we adopt elliptical Gaussian probability distribution in CenterNet to improve the performance of base detector models. Experimental results on the public SAR-Ship dataset show that our SAR-ShipNet achieves competitive advantages in both speed and accuracy.
