关键词: 卷积神经网络, 船舶检测, 合成孔径雷达

Abstract: SAR ship detection plays an important role in marine traffic monitoring. Traditional SAR target detection algorithms are mostly based on contrast difference between target and background clutter, whose performance is limited especially in complex scenes, for instance coastal areas. In order to improve the detection performance in complex scenes, a SAR ship detection algorithm based on Faster R-CNN is proposed in this paper. After analyzing the influence of feature resolution on detection performance, a feature extraction network suitable for SAR ship target detection is designed based on the idea of VGG and dilated convolution to improve the detection capability of small ship targets. In addition, a small size anchor is selected according to the target size distribution in the sentinel-1A dataset. And by removing the redundant anchor, the detection speed is improved. Experiments on the sentinel-1A dataset demonstrate that the proposed algorithm can detect ship targets in SAR images of complex scenes with high speed and accuracy.

Key words: convolutional neural network, ship detection, synthetic aperture radar