Dense Feature Pyramid Network for Ship Detection in SAR Images

摘要

Synthetic aperture radar (SAR) is an activate microwave imaging system, that provides images in all-day and all-weather conditions. The various scales and irregular distribution of different ships in SAR images, is a heated and difficult problem. As a basic component in the object detection frameworks, Feature Pyramid Networks (FPNs) improve feature representations for detecting objects at different scales. However, FPN adopts the same convolution operation at different layers, that does not consider the differences between different levels. We present Dense Feature Pyramid Network (DenseFPN) in this paper. Based on the hierarchy of backbone network, cross-scale connections and lateral connections, the shallow features and deep features are processed differently in DenseFPN. Compared with conventional FPN, we integrate DenseFPN into Faster R-CNN framework and thus form a novel detector. Experiments on high-resolution SAR images dataset (HRSID) have demonstrated the effectiveness of the enhanced hierarchical features in the proposed method compared with other typical methods.