Article(id=1284878610301423643, tenantId=1146029695717560320, journalId=1283840460791746584, issueId=1284096873799594072, articleNumber=PA20260711_ZLFR2NXO, orderNo=null, doi=10.13637/j.issn.1009-6094.2025.1419, 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=1784268532586, onlineDateStr=2026-07-17, pubDate=1782316800000, pubDateStr=2026-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1784268532585, onlineIssueDateStr=2026-07-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1784268532585, creator=admin, updateTime=1784268532585, updator=admin, issue=Issue{id=1284096873799594072, tenantId=1146029695717560320, journalId=1283840460791746584, year='2026', volume='26', issue='6', pageStart='null', pageEnd='null', issueExtLink='null', onlineDate='null', pubDate='1782316800000', pubDateStr='2026-06-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=0, articleOrder=1, issueType=-1, specialIssue=null, createTime=1784082152080, creator='ztmeta', updateTime=1784082152080, updator='ztmeta', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=null, endPage=null, ext={CN=ArticleExt(id=1284878617628872733, articleId=1284878610301423643, tenantId=1146029695717560320, journalId=1283840460791746584, language=CN, title=基于HIC-YOLO的井下安全帽检测算法研究, columnId=null, journalTitle=安全与环境学报, columnName=“新质生产力驱动的矿山安全与绿色转型技术”专题, runingTitle=null, highlight=null, articleAbstract=针对煤矿井下光照不足、遮挡频发和目标尺度偏小导致的安全帽检测漏检、误检及实时性下降问题，研究提出基于YOLOv5的改进模型HIC-YOLOv5。首先，在原始模型上增加高分辨率小目标检测头(Small Object Detection Head, SODH),以增强小目标特征提取能力；其次，在骨干网络与颈部网络之间引入通道特征融合模块(Channel Feature Fusion with Involution, CFFI),以缓解1×1降维造成的语义流失，强化跨通道信息交互，使更多关键信息传递至深层网络；最后，在骨干网络末端集成轻量级注意力机制(Convolutional Block Attention Module, CBAM),通过空间与通道双重加权突出安全帽区域特征。结果显示，在IoU为0.5的条件下，HIC-YOLOv5的mAP@50达88.33%,较基线YOLOv5提升4.73百分点，mAP@50:95提升至51.89%,P、R分别为90.81%、82.67%。在资源开销方面，参数量为8.4 MB,FLOPs为30.6 GB,推理速度为102帧/s。消融试验结果显示，3项改进具备协同增益，其中SODH对小目标召回提升最为显著。与YOLOv7、YOLOv8、YOLOv9、YOLOv11、YOLOv12对比，HIC-YOLOv5模型综合性能保持领先。HIC-YOLOv5在低照度、强遮挡与小目标密集的矿井安全帽检测场景中实现了更高的检测准确性与稳定性，并以中等计算量维持较好的实时性能，具备边缘部署可行性。, 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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