Article(id=1321893337578189503, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893272490987695, 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=1751212800000, receivedDateStr=2025-06-30, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793093531269, onlineDateStr=2026-10-27, pubDate=1781712000000, pubDateStr=2026-06-18, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793093531269, onlineIssueDateStr=2026-10-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793093531269, creator=13701087609, updateTime=1793093531269, updator=13701087609, issue=Issue{id=1321893272490987695, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='6', pageStart='1', pageEnd='312', issueExtLink='null', onlineDate='null', pubDate='1781712000000', pubDateStr='2026-06-18', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793093515751, creator='13701087609', updateTime=1793094575775, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321897718625194101, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893272490987695, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321897718625194102, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893272490987695, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=244, endPage=254, ext={EN=ArticleExt(id=1321893337754350272, articleId=1321893337578189503, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Flotation Foam Size Extraction Method Based on Improved YOLOv11n-seg, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Aiming at the difficulty of foam size extraction in flotation process, a flotation foam size extraction method based on improved YOLOv11n-seg was proposed. Firstly, the Cross-Scale Feature Fusion Module (CCFM) was introduced to optimize the neck structure and enhance the adaptability of the model to multi-scale foam and the ability of small target detection. Secondly, the Mixed Local Channel Attention (MLCA) mechanism was embedded, and the channel information, spatial information and local information were integrated to improve the expression ability of the network. Finally, the Unified-IoU loss function was introduced to enable the model to dynamically shift attention from the low-quality prediction box to the high-quality prediction box, balancing training speed and detection accuracy. The experimental results show that compared with the traditional watershed algorithm and Mask R-CNN based on deep learning, the proposed method has significant advantages in segmentation accuracy and real-time performance. Compared with the benchmark model YOLOv11n-seg, the optimized model improves the accuracy of mAP@ 0.5 and mAP@ 0.5∶0.95 by 1.5% and 5.3%, respectively, the recall rate increases by 5.1%, the amount of computation decreases by 11.8%, and the number of parameters decreases by 32.1%. This method has a faster detection speed and higher detection accuracy, which can meet the needs of accurate extraction of flotation foam size.

, authors=Li Guo, Zhiqi Huang, Runfeng He, Song Jiang, Caiwu Lu, authorsList=Li Guo, Zhiqi Huang, Runfeng He, Song Jiang, Caiwu Lu, authorCompany=null, correspAuthors=Zhiqi Huang, 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=1321893338152809153, articleId=1321893337578189503, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于改进YOLOv11n-seg的浮选泡沫尺寸提取方法, columnId=1226462297560892399, journalTitle=矿业研究与开发, columnName=选矿与资源综合利用, runingTitle=null, highlight=null, articleAbstract=

针对浮选工艺中泡沫尺寸提取的难题，提出了一种基于改进YOLOv11n-seg的浮选泡沫尺寸提取方法。首先，引进跨尺度特征融合模块（Cross-Scale Feature Fusion Module, CCFM）优化颈部结构，增强模型对多尺度泡沫的适应性与小目标检测能力；其次，嵌入混合局部通道注意力机制（Mixed Local Channel Attention, MLCA），整合通道信息、空间信息以及局部信息，提升网络的表达能力；最后，引入Unified-IoU损失函数，使模型动态地将注意力从低质量预测框转移到高质量预测框，平衡训练速度和检测精度。试验结果表明，相较于传统的分水岭算法和基于深度学习的Mask R-CNN等方法，所提出的方法在分割精度和实时性方面具有显著优势；与基准模型YOLOv11n-seg相比，优化后模型在精度mAP@0.5和mAP@0.5∶0.95上分别提升1.5%和5.3%，召回率提升5.1%，运算量减少11.8%，参数量下降32.1%，该方法具有更快的检测速度和更高的检测精度，能够满足浮选泡沫尺寸精准提取的需求。

, authors=郭梨, 黄智奇, 何润丰, 江松, 卢才武, authorsList=郭梨, 黄智奇, 何润丰, 江松, 卢才武, authorCompany=null, correspAuthors=黄智奇, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=ma/Ru9lDWjiP9jVuk727sg==, magXml=OiFTLVtbWOuCJmz6JkNh3w==, pdfUrl=null, pdf=LF+V6e+i2eNtJ/DRWTJ0TQ==, pdfFileSize=3838681, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=TDzqdIc3VuqiL++i3+WzEw==, mapNumber=null, fund=null)}, authors=[Author(id=1321893338660319942, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893337578189503, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=guoli@xauat.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1321893338840675016, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893337578189503, authorId=1321893338660319942, language=EN, stringName=Li Guo, firstName=Li, middleName=null, lastName=Guo, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=School of Resources Engineering, Xi'an University of Architecture and Technology, Xi'an, Shaanxi 710055, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1321893340493230793, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893337578189503, authorId=1321893338660319942, language=CN, stringName=郭梨, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=西安建筑科技大学 资源工程学院，陕西 西安　710055, bio={"content":"

郭梨（1982—），女，陕西西安人，博士，讲师，主要从事资源大数据管理与应用、矿山智能科学与工程方向的研究工作。E-mail:guoli@xauat.edu.cn

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郭梨（1982—），女，陕西西安人，博士，讲师，主要从事资源大数据管理与应用、矿山智能科学与工程方向的研究工作。E-mail:guoli@xauat.edu.cn

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