Article(id=1284878622234218566, tenantId=1146029695717560320, journalId=1283840460791746584, issueId=1284096873799594072, articleNumber=PA20260711_wTOrJKyb, orderNo=null, doi=10.13637/j.issn.1009-6094.2025.1156, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1784268535431, onlineDateStr=2026-07-17, pubDate=1782316800000, pubDateStr=2026-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1784268535430, onlineIssueDateStr=2026-07-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1784268535430, creator=admin, updateTime=1784268535430, updator=admin, issue=Issue{id=1284096873799594072, tenantId=1146029695717560320, journalId=1283840460791746584, year='2026', volume='26', 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YOLO,GPBW-YOLO)的钢丝绳表面损伤检测方法。首先，在Backbone部分引入GhostConv,通过去除冗余特征降低模型复杂度，提高特征提取效率；其次，将并行化补丁感知(Parallelized Patch-Aware Attention, PPA)注意力机制融入C3K2模块中，其中PPA注意力机制通过损伤区域的先验机制和对损伤特征的位置感知，有效提高了模型对损伤特征的关注度。再次，在Neck部分使用双向特征金字塔(Bidirectional Feature Pyramid Network, Bi FPN)机制进行多尺度特征融合，通过结合一种多尺度特征融合权重和跨越阶层式的融合方式，提高对不同尺度钢丝绳损伤特征的融合效率；最后，将WIoUv3作为模型的边界框损失函数，并引入一种动态梯度增益策略，该策略降低了高质量样本的竞争性和低质量样本的有害梯度，可在模型训练的过程中重点关注普通样本带来的梯度效益，进而提高模型在训练过程中的整体性能。试验结果显示，GPBW-YOLO模型mAP@0.5达到92.0%,帧率(Frames Per Second, FPS)达到119.0帧/s,较YOLOv11n分别提高了2.6百分点和8.4%;其参数量、计算量和模型存储量分别为1.9 MB、6.2 GB和4.2 MB,较YOLOv11n分别降低26.9%、1.6%和23.6%,满足对钢丝绳表面损伤检测的需求。研究为矿山智能运输体系建设提供了相关技术支撑，有助于推动矿山装备在智能化、安全化与绿色转型方向的发展。, authors=null, authorsList=null, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, 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