计算机工程2025，Vol.51Issue(12)：31-42,12.DOI:10.19678/j.issn.1000-3428.0070516
基于时序图像的双分支SAR图像船舶检测方法
Dual-Branch SAR Image Ship Detection Method Based on Time-Series Images
摘要
Abstract
Convolutional neural network-based object detection algorithms have achieved significant progress in ship detection using Synthetic Aperture Radar(SAR)images.However,in inshore scenes,strong scattering signals from coastlines,buildings,and other background clutter limit ship detection accuracy.To address this issue,this study proposes a dual-branch network-based SAR ship detection method.This method uses pseudo-background information extracted from time-series SAR images in one branch,combines it with the ship target image is processed in the other branch,and inputs them into a backbone network for parallel feature extraction.The ship detection capability is further enhanced through a feature fusion model that utilizes dual-branch features.In addition,a Feature Enhancement Module(FEM)is introduced that employs an attention mechanism and a Feature Fusion and Alignment Module(FFAM)to optimize and refine the feature maps,thereby enriching semantic information in shallow features and achieving finer fusion between hierarchical features.A Dynamic Gated Module(DGM)is applied in the dual-branch feature fusion strategy,generating dynamic gated weights to adaptively adjust the fusion ratio and enhance focus on the target features.Experimental results on a temporal SAR image ship dataset show that compared with mainstream bounding box detection methods,such as YOLOv11 and YOLOv8,the proposed method achieves the highest Average Precision(AP).In inshore scenes with densely populated objects,it achieves high recall and precision rates and is able to detect ships docked at the coastline more accurately.关键词
船舶检测/合成孔径雷达时序影像/双分支深度网络/特征对齐/动态门控机制Key words
ship detection/Synthetic Aperture Radar(SAR)time-series imagery/dual-branch deep network/feature alignment/dynamic gated mechanism分类
信息技术与安全科学引用本文复制引用
FAN Yiying,GUO Wei..基于时序图像的双分支SAR图像船舶检测方法[J].计算机工程,2025,51(12):31-42,12.基金项目
国家自然科学基金(42071431). （42071431）
