GLDet: Real-Time SAR Ship Detector Based on Global Semantic Information Enhancement and Local Gradient Information Mining

Abstract

Detecting ships in Synthetic Aperture Radar (SAR) images is a challenging task due to various factors, such as the diverse distribution of ships and the intricate nature of SAR images. In recent years, deep learning has made excellent progress in the field of SAR interpretation. Models that focus on extracting global semantic information can effectively achieve balanced detection of multi-scale SAR targets, but their computational complexity is relatively high. Models that focus on processing local information have redundant calculations and poor robustness, but are prone to mistaking the background information of SAR images for targets. To address the above issues, we propose a real-time SAR ship detector based on global semantic information enhancement and local gradient information mining. The lightweight feature extraction backbone based on linear computing is designed, with the network structure of Global Information Augmentation Encoder (GIAE)—Local Gradient Information Miner (LGIM)—Decoder, which can quickly perform feature extraction. GIAE enhances the expression of image content through the long sequence modeling capability of the State Space Model. LGIM uses gradient modules composed of depthwise separable convolutions to extract local information of image, and utilizes directed self-attention (DSA) to mine channel context information. GLDet can complete object detection, rotated object detection and instance segmentation tasks by transforming the detection head. Excellent performance has been achieved on the SAR ship instance segmentation dataset SSDD and HRSID, as well as the SAR rotated ship dataset RSDD-SAR and SSDD+. Meanwhile, GLDet demonstrated excellent generalization performance in large-scale SAR images captured by GF-3 and Terra-SAR satellites.

Topics & Concepts