Article(id=1240655133906882814, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655131507740845, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2025.03.017, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1722182400000, receivedDateStr=2024-07-29, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773724834180, onlineDateStr=2026-03-17, pubDate=1758729600000, pubDateStr=2025-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773724834180, onlineIssueDateStr=2026-03-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773724834180, creator=13701087609, updateTime=1773724834180, updator=13701087609, issue=Issue{id=1240655131507740845, tenantId=1146029695717560320, journalId=1240618002186551303, year='2025', volume='48', issue='3', pageStart='1', pageEnd='184', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773724833608, creator=13701087609, updateTime=1773725188865, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1240656621609742880, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655131507740845, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1240656621613937185, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1240655131507740845, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=137, endPage=146, ext={EN=ArticleExt(id=1240655134124986639, articleId=1240655133906882814, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Remote sensing image ship target detection algorithm based on improved YOLOv7, columnId=1240655021340160960, journalTitle=Navigation of China, columnName=Intelligent Shipping, runingTitle=null, highlight=null, articleAbstract=

To address challenges such as significant scale variations, high aspect ratios, dense arrangements, and complex backgrounds in ship target detection from remote sensing images, this paper proposes an improved YOLOv7-based algorithm. Using YOLOv7 as the baseline network, the prior anchor generation algorithm is optimized for the dataset. A long-edge representation method combined with circular smooth labeling is introduced to detect ship targets with uncertain rotation directions. The YOLOv7 network is enhanced by embedding both the GAM (Global Attention Mechanism) and SimAM (Simple Attention Mechanism) modules, which effectively suppress interference from complex background regions in remote sensing images and improve target detection accuracy. Additionally, the coordinate loss function is optimized to accelerate model convergence. Experimental results on the DOTA-ship and HRSC2016 datasets for both single-class and multi-class detection tasks show mAP values of 86.1%, 97.7%, and 87.1%, respectively-representing improvements of 7.8%, 4.6%, and 7.9% over the original YOLOv7 model. These results validate the effectiveness and superiority of the proposed method.

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针对遥感图像中舰船目标存在尺度变化大、大长宽比、排列密集、背景信息复杂等特点，提出了一种基于改进YOLOv7的遥感图像舰船目标检测算法。以YOLOv7作为基线网络，优化数据集先验锚框生成算法；使用长边表示法结合圆形平滑标签方法，解决边界回归的周期性带来的突变问题；并将全局注意力（GAM）与无参注意力（SimAM）嵌入到YOLOv7网络中，有效消弱遥感图像中复杂背景区域信息带来的干扰，提高目标检测准确率；优化坐标框损失函数，加快模型收敛速度。通过对舰船数据集（DOTA-ship），以及HRSC2016数据集执行单类检测与多类检测任务，mAP结果分别为86.1%、97.7%、87.1%，相比于原YOLOv7分别提升了7.8%、4.6%、7.9%，验证了方法的有效性与优越性。

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董紫微（1999—），女，硕士研究生，研究方向为计算机视觉。E-mail:dzw201712@163.com

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