大场景SAR影像舰船目标检测的轻量化研究  

Lightweight Research on Ship Target Detection in Large-scale SAR Images

作　　者：张尧 张艳[1] 王涛[1,2] 王步云 ZHANG Yao;ZHANG Yan;WANG Tao;WANG Buyun(Information Engineering University,Zhengzhou 450001,China;Key Laboratory of Spatiotemporal Perception and Intelligent Processing Ministry of Natural Resource,Zhengzhou 450001,China)

机构地区：[1]信息工程大学,郑州450001 [2]时空感知与智能处理自然资源部重点实验室,郑州450001

出　　处：《地球信息科学学报》2025年第1期256-270,共15页Journal of Geo-information Science

基　　金：装备综合研究科研项目(a8203)。

摘　　要：【目的】基于合成孔径雷达(SAR)影像的舰船检测已广泛用于海洋搜救、港口侦察、领海防御等领域。然而,随着在轨智能处理技术的快速发展,对星载SAR影像舰船目标的实时检测能力提出了更高的要求。【方法】因此针对当前SAR影像舰船目标尺度多样,靠岸船只背景复杂,各类遥感平台硬件资源受限等问题,本文提出了一种轻量级SAR影像舰船检测模型LWM-YOLO。首先,采用优化骨干网络结合注意力机制降低网络复杂度及参数量,提出一个轻量化骨干网络(LWCA);其次,针对解决目标尺度多样问题,构建了一个轻型特征融合模块(LGS-FPN),在增强SAR影像中舰船目标细节信息提取能力的同时减少计算复杂度;然后,为优化定位精度,提出基于MPD-Head的检测架构,提升复杂环境中微小舰船目标的检测效果;最后,在LS-SSDD和SSDD舰船目标检测数据集上,将本文算法与主流算法进行对比实验。【结果】实验结果表明,本文提出的算法平均精度值(mAP)分别达到了74.7%和97.3%,较基础模型提升了1.5%和1.0%。同时,本文所提算法参数规模缩减至原模型的36%,计算复杂度降至80%。【结论】与其他方法相比,本文提出的算法不仅在精度上有所提升,而且在检测速率上也具有显著优势。这一研究成果可为智能化目标检测、空间在轨应用等领域提供有力支撑。[Objectives]Ship detection using Synthetic Aperture Radar(SAR)images has gained widespread recognition and application across various fields,including marine search and rescue,port reconnaissance,and territorial sea defense.Nevertheless,with the rapid advancement of on-orbit intelligent processing technologies,higher demands have emerged for real-time detection of ship targets in spaceborne SAR images.[Methods]To address challenges such as the diverse scales of ship targets in current SAR images,the complex background of shore-based vessels,and the limited hardware resources of various remote sensing platforms,this paper presents a lightweight SAR image ship detection model,LWM-YOLO.Firstly,we propose a Lightweight Backbone Network(LWCA)designed specifically for SAR image processing.The LWCA integrates an optimized backbone network with an attention mechanism,effectively reducing the model's complexity and parameter size while maintaining high performance and lowering computational demands.Secondly,to tackle the issue of diverse target scales in SAR images,we have constructed a lightweight feature fusion module,termed LGS-FPN.This module enhances the extraction of detailed information on ship targets in SAR images by efficiently fusing features from different scales,improving detection performance for ship targets of various sizes.Furthermore,the module minimizes computational complexity,ensuring that the model can operate smoothly without significant resource consumption.In addition to addressing the scale issue,we have also focused on optimizing localization accuracy.We introduce a detection architecture based on the MPD-Head,which leverages the strengths of the MPD-Head to improve detection performance for small ship targets in complex environments.Finally,we validate the proposed algorithm through comparative experiments with mainstream methods on the LS-SSDD and SSDD ship detection datasets.[Results]The results demonstrate that our algorithm achieved mean Average Precision(mAP)values of 74.7%and 97.3%on the

关 键 词：合成孔径雷达 目标检测 轻量化 深度学习 舰船检测 大场景影像 YOLOv8 

分 类 号：U675.79[交通运输工程—船舶及航道工程] E91[交通运输工程—船舶与海洋工程] TN957.52[军事]