Abstract
Ship detection of synthetic aperture radar (SAR) images is one of the research hotspots in the field of marine surveillance. Fusing salient features to detection network can effectively improve the precision of ship detection. However, how to effectively fuse the salient features of SAR images is still a difficult task. In this paper, to improve the ship detection precision, we design a novel one-stage ship detection network to fuse salient features and deep convolutional neural network (CNN) features. Firstly, a saliency map extraction algorithm is proposed. The algorithm is applied to generate saliency map by using multi-scale pyramid features and frequency domain features. Secondly, the backbone of the ship detection network contains a two-stream network. The upper-stream network uses the original SAR image as input to extract multi-scale deep CNN features. The lower-stream network uses the corresponding saliency map as input to acquire multi-scale salient features. Thirdly, for integrating the salient features to deep CNN features, a novel salient feature fusion method is designed. Finally, an improved bi-directional feature pyramid network is applied to the ship detection network for reducing the computational complexity and network parameters. The proposed methods are evaluated on the public ship detection dataset and the experimental results shows that it can make a significant improvement in the precision of SAR image ship detection.
Highlights
With the expansion of maritime rights and interests, it has become important to have a comprehensive, timely and accurate grasp of ocean dynamics
The results show that the proposed methods can make a big leap forward in improving the performance of synthetic aperture radar (SAR) image ship detection
PROPOSED METHOD The proposed one-stage ship detection network fuses the salient features and deep convolutional neural network (CNN) features for improving the ship detection precision in SAR images
Summary
With the expansion of maritime rights and interests, it has become important to have a comprehensive, timely and accurate grasp of ocean dynamics. G. Zhang et al.: Novel Salient Feature Fusion Method for Ship Detection reserved from first stage to obtain the category probability and target position. We propose a novel one-stage detection network for fusing salient features and deep CNN features. A novel one-stage ship detection network is proposed to fuse salient features and deep CNN features. Aiming at the characteristics of SAR images, a novel saliency map extraction algorithm is designed It combines multi-scale pyramid features and frequency domain features to generate saliency maps. For integrating salient features to deep CNN features without adding feature dimensions and network parameters, a novel salient fusion method is proposed. The results show that the proposed methods can make a big leap forward in improving the performance of SAR image ship detection.
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