GL-DETR: Global-to-Local Transformers for Small Ship Detection in SAR Images
Cong Li, Yongqiang Hei, Lihu Xi, Wentao Li, Zhu Xiao
Abstract
Transformer-based methods have demonstrated their potential capabilities in ship detection of synthetic aperture radar (SAR) images. However, their exclusive reliance on global context information hinders the precise localization of small ships, leading to suboptimal detection performance. In this work, a global-to-local detection transformer (GL-DETR) framework is proposed to enhance the detection accuracy of small ships in SAR images. In GL-DETR, the decoder is constituted of a global layer and a carefully designed local layer. Within the global layer, object queries fully interact with global context information. Furthermore, a local interaction attention (LIA) module is designed in the local layer, with the purpose of refining and enriching the object query features through local multiscale region of interest (ROI) information. Additionally, a multiscale information enhancement (MIE) module is introduced to enhance the high-frequency information of small ships through Gaussian filtering. Numerical results demonstrate that GL-DETR outperforms baseline by 3.71% and 4.89% on classical SAR datasets LS-SSDD and HRSID, respectively. These results demonstrate the effectiveness and superiority of the proposed strategy.