面向SAR图像舰船检测的多粒度特征与形位相似度量方法  

Multi-granularity feature and shape-position similarity metric method for ship detection in SAR images

作　　者：李士博 肖振久[1] 曲海成[1] 李富坤 王晶晶 Li Shibo;Xiao Zhenjiu;Qu Haicheng;Li Fukun;Wang Jingjing(School of Software,Liaoning Technical University,Huludao,Liaoning 125105,China;College of Computer and Information Engineering,Henan Normal University,Xinxiang,Henan 453007,China;School of Geophysics and Geomatics,China University of Geosciences,Wuhan,Hubei 430074,China)

机构地区：[1]辽宁工程技术大学软件学院,辽宁葫芦岛125105 [2]河南师范大学计算机与信息工程学院,河南新乡453007 [3]中国地质大学地球物理与空间信息学院,湖北武汉430074

出　　处：《光电工程》2025年第2期44-58,共15页Opto-Electronic Engineering

基　　金：辽宁省高等学校基本科研项目(LJKMZ20220699);辽宁工程技术大学学科创新团队资助项目(LNTU20TD-23)。

摘　　要：针对合成孔径雷达(SAR)图像背景复杂、目标尺度变化大,尤其在小目标密集场景中容易出现误检和漏检问题,提出一种面向SAR图像舰船检测的多粒度特征与形位相似度量方法。在特征提取阶段,设计包含双分支多粒度特征聚合结构。一个分支通过Haar小波变换对特征图级联分解,以扩大全局感受野,从而提取粗粒度特征;另一分支引入空间和通道重建卷积,用于捕捉细节纹理信息,以减少特征图的上下文信息损失。两分支通过协同利用局部和非局部特征的相互作用,有效抑制复杂背景和杂波干扰,实现多尺度特征的精确提取。在检测回归阶段,利用欧几里得距离,并结合位置与形状信息,提出形位相似度量方法,以解决小目标密集场景中位置偏差敏感性问题,从而平衡正负样本的分配。在SSDD和HRSID数据集上与双阶段、单阶段及DETR系列共11种检测器进行综合对比,本文方法在两数据集上mAP和mAP50分别达到68.8%、98.3%和70.8%、93.8%。此外,模型参数量仅为2.4 M,计算量为6.4 GFLOPs,优于对比方法。本文方法在复杂背景和不同尺度舰船目标下表现出优异的检测性能,在降低误检率和漏检率的同时,具有更低的模型参数量和计算量。To address the challenges of background complexity and target scale changes in synthetic aperture radar(SAR)images,especially in densely populated small-target scenes prone to false and missed detections,a multi-granularity feature and shape-position similarity metric method for ship detection in SAR images is proposed.First,a multi-granularity feature aggregation structure containing two branches is designed in the feature extraction stage.One branch decomposes the feature map cascade by Haar wavelet transform to expand the global receptive field to extract coarse-grained features.The other branch introduces spatial and channel reconstruction convolution to capture detailed texture information,thereby minimizing the loss of contextual information.The two branches effectively suppress the complex background and clutter interference by synergistically exploiting the interaction of local and non-local features to achieve accurate extraction of multi-scale features.Next,by utilizing the Euclidean distance and combining position and shape information,we propose a shape-position similarity metric to solve the problem of position deviation sensitivity in small target-dense scenes,thereby balancing the allocation of positive and negative samples.In a comprehensive comparison with 11 detectors from one-stage,two-stage,and DETR series on the SSDD and HRSID datasets,our method achieves mAP scores of 68.8%and 98.3%,and mAP50 scores of 70.8%and 93.8%,respectively.In addition,our model is highly efficient,with just 2.4 M parameters and a computational load of only 6.4 GFLOPs,outperforming the comparison methods.The proposed method shows excellent detection performance under complex backgrounds and ship targets of different scales.While reducing the false detection rate and missed detection rate,it has a low model parameter amount and computational complexity.

关 键 词：SAR图像 舰船检测 特征提取 小波变换 欧几里得距离 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]