FEATURE-TRANSFERABLE PYRAMID NETWORK FOR DENSE MULTI-SCALE OBJECT DETECTION IN SAR IMAGES

In synthetic aperture radar (SAR) images, there are a large number of dense multi-scale objects, especially dense multi-scale ships docked along the coast. Existing object detection methods are difficult to simultaneously detect dense multiscale objects in complex background. A novel method for dense multi-scale object detection in SAR images based on Feature-Transferable Pyramid Network (FTPN) is proposed in this paper. In the stage of feature extraction, the feature maps of each layer are connected effectively and the feature maps of various scales are extracted. This method can extract the features of dense multi-scale objects more effectively, so as to realize simultaneous detection of dense multi-scale objects in SAR images. Experiments on SSDD dataset, AIR-SARShip-2.0 dataset and Gaofen-3 dataset show that the proposed method can achieve dense multi-scale object detection, and the overall performance is better than the state-of-the-art methods.

Automated summary

This paper proposes a novel method for dense multi-scale object detection in SAR images based on Feature-Transferable Pyramid Network (FTPN). It effectively connects the feature maps of each layer and extracts feature maps of various scales to enhance the detection of dense multi-scale objects in complex backgrounds.