Dense feature pyramid network for ship detection in SAR images
Weihua Hu, Zhuangzhuang Tian, Shiqi Chen, Ronghui Zhan, Jun Zhang · 2020
Synthetic aperture radar (SAR) is an active microwave imaging sensor, which can provide images in all-weather and all-day conditions. The various scales and irregular distribution of different ships in SAR images, is a heated and challenging 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, which does not consider the differences between different levels. In this paper, we present Dense Feature Pyramid Network (DenseFPN). Based on the hierarchy of backbone network, the 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 verified the effectiveness of the enhanced hierarchical feature in the proposed method compared with other typical CNN based methods.