Article(id=1282034780761985479, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282034747870253265, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.10.1312, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1744214400000, receivedDateStr=2025-04-10, revisedDate=1752163200000, revisedDateStr=2025-07-11, acceptedDate=null, acceptedDateStr=null, onlineDate=1783590510788, onlineDateStr=2026-07-09, pubDate=1761580800000, pubDateStr=2025-10-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783590510788, onlineIssueDateStr=2026-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783590510788, creator=13701087609, updateTime=1783590510788, updator=13701087609, issue=Issue{id=1282034747870253265, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='10', pageStart='1', pageEnd='252', issueExtLink='null', onlineDate='null', pubDate='1761580800000', pubDateStr='2025-10-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783590502946, creator='13701087609', updateTime=1783593819616, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282048659068006772, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282034747870253265, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282048659068006773, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282034747870253265, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=190, endPage=197, ext={EN=ArticleExt(id=1282034780971700680, articleId=1282034780761985479, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Improved U-Net-based model for urban flood disaster image recognition, columnId=1149735802789348081, journalTitle=China Safety Science Journal, columnName=Technology and engineering of disaster prevention and mitigation, runingTitle=null, highlight=null, articleAbstract=

In order to address the challenges of inaccurate region segmentation and insufficient detail restoration in flood disaster recognition models within complex urban environments, AttResU-Net, an enhanced U-Net semantic segmentation model integrating residual networks and a self-attention mechanism was proposed. Building upon the classical U-Net architecture, the model employed a deep residual network as the encoder to strengthen feature representation. Simultaneously, self-attention mechanisms were incorporated into the decoder to enhance response capability on key flood-related regions. A comprehensive training and testing pipeline was established. The improved AttResU-Net was trained and evaluated on the FloodNet dataset, which contains diverse and complex urban environmental categories. Quantitative metrics and qualitative visual results demonstrate the model's superior performance, achieving a mean pixel accuracy (mPA) of 79.75%, pixel accuracy (PA) of 90.01%, and mean precision (mPrecision) of 81.78%. Comparative experiments against state-of-the-art models reveal that AttResU-Net attains significantly higher segmentation accuracy and global recognition capability, particularly for urban features such as trees, water bodies, roads, and buildings.

, authors=Xingrun ZHONG¹, Chenbin TIAN^{**, 1}, Xinhong LI¹, Xiaojing MENG^{1, 2}, Wenxin YANG¹, authorsList=Xingrun ZHONG, Chenbin TIAN, Xinhong LI, Xiaojing MENG, Wenxin YANG, authorCompany=null, correspAuthors=Chenbin TIAN, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1282034781500182987, articleId=1282034780761985479, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于改进U-Net的城市洪涝灾害图像识别模型, columnId=1149735802885817075, journalTitle=中国安全科学学报, columnName=防灾减灾技术与工程, runingTitle=null, highlight=null, articleAbstract=

为解决洪涝灾害识别模型在城市复杂背景下区域分割不清和细节还原不足等问题，提升洪涝灾害图像识别准确性，提出一种基于残差网络和自注意力机制的改进U-Net语义分割模型——AttResU-Net模型。该模型在经典U-Net网络架构基础上进行优化设计，采用深层残差网络作为编码器以增强特征表达能力，同时在解码器中引入注意力机制，以提高对关键洪涝区域的响应能力；构建完整的训练与测试流程，使用FloodNet多类别复杂环境数据集训练改进AttResU-Net模型，从定量指标和定性可视化效果2个维度来评估模型性能，并与现有主流模型进行对比分析。结果表明：AttResU-Net模型在平均像素准确率（mPA）、像素准确率（PA）、平均精度（mPrecision）等指标上表现优异，其中，mPA为79.75%、PA为90.01%、mPrecision为81.78%；相比其他模型，AttResU-Net模型在树木、水体、道路和建筑物等识别中表现出更高的分割准确率、全局像素精度和全局识别能力。

, authors=钟兴润讲师¹, 田晨斌^{**, 1}, 李新宏教授¹, 孟晓静教授^{1, 2}, 杨文欣¹, authorsList=钟兴润讲师, 田晨斌, 李新宏教授, 孟晓静教授, 杨文欣, authorCompany=null, correspAuthors=田晨斌, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=CSTUdAXZlVmbS/yZkvjhVQ==, magXml=bEuZVEW9lftg5PYIk1qV0g==, pdfUrl=null, pdf=X8xYOsRIqL6W0uAP2PHYfA==, pdfFileSize=6167267, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=Dw6co1LAwbMOr2dyfEhpTA==, mapNumber=null, fund=null)}, authors=[Author(id=1282034782058025427, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282034780761985479, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=283808185@qq.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1282034782146105813, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282034780761985479, authorId=1282034782058025427, language=EN, stringName=Xingrun ZHONG, firstName=Xingrun, middleName=null, lastName=ZHONG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹School of Resource Engineering, Xi'an University of Architecture and Technology, Xi'an Shaanxi, 710055, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1282034782225797590, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282034780761985479, authorId=1282034782058025427, language=CN, stringName=钟兴润讲师, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹西安建筑科技大学 资源工程学院，陕西 西安 710055, bio={"content":"

钟兴润（1984—），女，陕西榆林人，硕士，讲师，主要从事建筑安全工程、土木工程建造与管理、结构安全检测与鉴定等方面的研究。E-mail:283808185@qq.com。

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钟兴润（1984—），女，陕西榆林人，硕士，讲师，主要从事建筑安全工程、土木工程建造与管理、结构安全检测与鉴定等方面的研究。E-mail:283808185@qq.com。

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