计算机技术与发展2026，Vol.36Issue(5)：45-53,9.DOI:10.20165/j.cnki.ISSN1673-629X.2025.0325
EFEFMamba-YOLO:SAR图像中船舶目标检测
EFEFMamba-YOLO:Ship Target Detection in SAR Images
摘要
Abstract
To address the challenges in ship target detection from Synthetic Aperture Radar(SAR)images,such as small target size,dense distribution,blurred features,and complex background interference,we propose an improved model based on Mamba-YOLO,namely EFEFMamba-YOLO(Enhanced Feature Extraction and Fusion Mamba-YOLO).Firstly,to enhance feature extraction capability,a Collaborative Feature Enhancement Block(CFEBlock)is designed in the backbone network of Mamba-YOLO,which can effectively capture the local and global feature dependencies.Secondly,to tackle the problem of easy loss of detailed information during feature fusion,a Channel-Enhanced Residual Spatial Pyramid Pooling Fast(CResSPPF)module is developed at the end of the backbone network.This module preserves detailed information through a residual structure and improves feature expression ability by means of a channel expansion strategy.Finally,a Four-level Adaptive Structure Feature Fusion(FASFF)detection head is designed,which effectively enhances the fusion effect of features at different levels.Experiments are conducted on the HRSID,SSDD,and LS-SSDD-v1.0 datasets.The results show that the mAP50 of EFEFMamba-YOLO reaches 94.1%on HRSID,an improvement of 1.9 percentage points compared with the baseline Mamba-YOLO model.On the SSDD and LS-SSDD-v1.0 datasets,the mAP50 values of the EFEFMamba-YOLO model reach 98.9%and 75.6%,respectively.Experimental results demonstrate that the EFEFMamba-YOLO model exhibits excellent effectiveness and reliability in ship target detection from SAR images.关键词
合成孔径雷达/船舶图像/目标检测/Mamba-YOLO/协同特征增强模块Key words
SAR/ship images/target detection/Mamba-YOLO/CFEBlock分类
信息技术与安全科学引用本文复制引用
贾涛阳,王浩,王雪铭,张嘉薇,黄敏..EFEFMamba-YOLO:SAR图像中船舶目标检测[J].计算机技术与发展,2026,36(5):45-53,9.基金项目
国防科技重点实验室基金(6142205240201) （6142205240201）
