Scale Expansion Pyramid Network for Cross-Scale Object Detection in Sar Images
Zheng Ou Zhou, Rui Yi Guan, Zongyong Cui, Zongjie Cao, Yiming Pi, Jianyu Yang · 2021
In SAR images, there are objects with large scale difference, which is called cross-scale objects. For example, there are both large-scale airport objects and small-scale ship objects in SAR images. However, the current multi-scale object detection methods are difficult to detect objects with large scale difference. To address this issue, we propose a cross-scale object detection method for SAR images based on Scale Expansion Pyramid Network(SEPN) in this paper. The proposed SEPN can extract the salient features of the objects with a large scale difference, and by closely connecting the scale expansion layer with the convolutional layer during the downsampling process of the Feature Pyramid Network (FPN), the receptive field of the feature extracted by the convolutional layer can be adaptively extended, and finally it achieves the effect of cross-scale object detection in SAR image. Experiments on SSDD dataset and Gaofen-3 dataset show the effectiveness of our proposed methods in detecting objects of different scales in different scenes of SAR images.