Article(id=1322559888262279669, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559853093048851, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1737475200000, receivedDateStr=2025-01-22, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793252449335, onlineDateStr=2026-10-29, pubDate=1771948800000, pubDateStr=2026-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793252449335, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793252449335, creator=13701087609, updateTime=1793252449335, updator=13701087609, issue=Issue{id=1322559853093048851, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='2', pageStart='1', pageEnd='326', issueExtLink='null', onlineDate='null', pubDate='1771948800000', pubDateStr='2026-02-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793252440951, creator='13701087609', updateTime=1793252699964, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322560939526828556, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559853093048851, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322560939526828557, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322559853093048851, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=288, endPage=302, ext={EN=ArticleExt(id=1322559888421663222, articleId=1322559888262279669, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Borehole Structural Plane Segmentation and Parameter Visualization Method with Dual-Encoder Attention Network, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Aiming at the problems of uneven illumination, complex background and large difference in structural plane morphology of borehole images, a dual-encoder interactive attention enhanced fusion network ( DISA-FuseNet ) was proposed. Based on the UNet encoder-decoder architecture, the network adopted the dual-encoder structure of Swin Transformer and ResNet50 to capture global context information and local detail features simultaneously. The attention was weighted by the Statistical Aggregation Channel Attention ( SACA ) module to suppress the background noise and irrelevant information of the rock mass. The underlying features were inputted into the Adaptive Hierarchical Enhancement module ( AHE ) after splicing to enhance the network's ability to capture multi-scale structural plane features. Experimental verification was conducted based on the self-constructed borehole structural plane dataset, and DISA-FuseNet was compared with five models, namely UNet, Deeplabv3+, Trans-UNet, DeepCrack and CrackFormer. The results show that DISA-FuseNet outperforms all the other models in terms of mean intersection over union, mean pixel accuracy, recall, precision and F1-score, these indexes are increased by 8.44, 9.51, 8.52, 10.60 and 9.62 percentage points respectively compared with those of the baseline model UNet. Furthermore, a method for structural plane parameter extraction and 3D visualization is proposed, which enables the accurate extraction of structural plane parameters and the construction of an interactive 3D visualization model. The actual engineering case verifies that the method has good effectiveness and engineering applicability.

, authors=Yiru SHI^{1, 2}, Dong DUAN^{1, 2}, Jie LI^{1, 2}, Xiaojing FENG^{1, 2}, Jiaxiong HE^{1, 2}, Weidong LU^{3, 4}, Yuanping GAN^{3, 4}, authorsList=Yiru SHI, Dong DUAN, Jie LI, Xiaojing FENG, Jiaxiong HE, Weidong LU, Yuanping GAN, authorCompany=null, correspAuthors=Dong DUAN, 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=1322559888681710071, articleId=1322559888262279669, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=融合双编码器注意力网络的钻孔结构面分割与参数可视化方法, columnId=1226462295946085268, journalTitle=矿业研究与开发, columnName=矿山机电与矿业智能化, runingTitle=null, highlight=null, articleAbstract=

针对钻孔图像光照不均、背景复杂及结构面形态差异大等难题，提出了一种双编码器交互式注意力增强融合网络（DISA-FuseNet）。该网络基于UNet编码器－解码器架构，采用Swin Transformer和ResNet50双编码器结构，同时捕捉全局上下文信息与局部细节特征；通过统计聚合通道注意力模块（SACA）进行注意力加权，抑制岩体背景噪声和无关信息；底层特征经拼接后输入自适应层级增强模块（AHE），强化网络对多尺度结构面特征的捕捉能力。基于自建钻孔结构面数据集开展试验验证，将DISA-FuseNet与UNet、Deeplabv3+、Trans-UNet、DeepCrack及CrackFormer五种模型进行对比。结果表明，DISA-FuseNet在平均交并比、平均像素准确率、召回率、精确率、F1分数指标上均优于其他模型，各指标较基准模型UNet分别提升了8.44，9.51，8.52，10.60，9.62个百分点。进一步提出结构面参数提取与三维可视化方法，实现结构面参数的精准提取与交互式三维可视化模型构建。经实际工程案例验证，该方法具备良好的有效性和工程适用性。

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时乙入（2000—），女，山西运城人，硕士研究生，主要研究方向为图像分割、结构面特征参数数据分析。E-mail:Shiyiru1213@gmail.com

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时乙入（2000—），女，山西运城人，硕士研究生，主要研究方向为图像分割、结构面特征参数数据分析。E-mail:Shiyiru1213@gmail.com

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