Article(id=1324333597053813177, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1324333573712511176, articleNumber=null, orderNo=null, doi=10.12398/j.issn.2096-7217.2026.01.007, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1762012800000, receivedDateStr=2025-11-02, revisedDate=1765728000000, revisedDateStr=2025-12-15, acceptedDate=null, acceptedDateStr=null, onlineDate=1793675334465, onlineDateStr=2026-11-03, pubDate=1771084800000, pubDateStr=2026-02-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793675334465, onlineIssueDateStr=2026-11-03, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793675334465, creator=13701087609, updateTime=1793675334465, updator=13701087609, issue=Issue{id=1324333573712511176, tenantId=1146029695717560320, journalId=1319690209941618849, year='2026', volume='7', issue='1', pageStart='1', pageEnd='196', issueExtLink='null', onlineDate='null', pubDate='1771084800000', pubDateStr='2026-02-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793675328901, creator='13701087609', updateTime=1793872389599, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1325160106287039215, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1324333573712511176, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1325160106287039216, tenantId=1146029695717560320, journalId=1319690209941618849, issueId=1324333573712511176, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=63, endPage=74, ext={EN=ArticleExt(id=1324333597460660666, articleId=1324333597053813177, tenantId=1146029695717560320, journalId=1319690209941618849, language=EN, title=Design and experiment of an intelligent recognition and detection model for grassland rodent burrows based on improved RT-DETR, columnId=null, journalTitle=Journal of Intelligent Agricultural Mechanization, columnName=null, runingTitle=null, highlight=null, articleAbstract=
Abstract: In recent years, the rapid proliferation and expansion of rodent burrows have become one of the major causes of grassland degradation and ecological imbalance. The dense distribution of burrows destroys turf structure, weakens soil stability, reduces vegetation coverage and forage productivity, and seriously threatens the ecological security and sustainable development of animal husbandry in grassland areas. To address the problems of insufficient small-target recognition, inadequate feature extraction, and significant background interference in existing detection algorithms, this study proposes an improved real-time detection model, RT-DETR-ECG (efficient global perception and feature selection), based on the Real-Time Detection Transformer (RT-DETR) framework. The model first introduces the Efficient Vision Mamba (EfficientVIM) module, which achieves efficient fusion of global and local features through state-space modeling and multi-scale feature interaction, thereby enhancing the perception of small targets. Second, it constructs a Convolutional Gated Linear Unit (CGLU) module, which dynamically suppresses complex background noise via the synergy of convolution and gating mechanisms, effectively highlighting the edges and textures of burrows. Finally , it designs a small-object weighted loss function (CGLU-Loss), which introduces center-offset weighting and local-overlap gain terms in the regression stage to strengthen gradient contributions from small-scale samples and improve localization precision. Experiments based on a dataset of 2 397 cross-season UAV images of grassland burrows demonstrate that RT-DETR-ECG outperforms existing models in detection accuracy, real-time performance, and lightweight efficiency. With an IoU threshold of 0.5, the model achieves a mean average precision (mAP@0.5) of 96.3%, 5.1% higher than the original RT-DETR, with a detection speed of 91.8 f/s and a 53.7% reduction in computational load. These results verify that RT-DETR-ECG effectively balances detection accuracy and efficiency, providing a reliable and efficient approach for intelligent grassland rodent burrow detection and ecological monitoring.
, authors=Zhenwei DONG, Xueliang FU, Honghui LI, Xin PAN, Zhe XU, Xiaoling LUO, authorsList=Zhenwei DONG, Xueliang FU, Honghui LI, Xin PAN, Zhe XU, Xiaoling LUO, authorCompany=null, correspAuthors=Xiaoling LUO, authorNote=
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, 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=1324333598240801211, articleId=1324333597053813177, tenantId=1146029695717560320, journalId=1319690209941618849, language=CN, title=基于改进RT-DETR的草原鼠洞智能识别与检测模型设计与试验, columnId=0, journalTitle=智能化农业装备学报（中英文）, columnName=, runingTitle=null, highlight=null, articleAbstract=
近年来，草原鼠洞的迅速增殖与扩散已成为导致草原退化和生态失衡的重要因素之一。鼠洞的密集分布破坏了草皮结构，削弱土壤稳定性，降低植被覆盖度与牧草产能，严重威胁草原生态安全与畜牧业的可持续发展。针对现有检测算法在小目标识别能力不足、特征提取不充分及复杂背景干扰显著等问题，研究提出一种基于实时检测（real-time detection transformer, RT-DETR）的改进模型——RT-DETR-ECG（efficient global perception and feature selection）。该模型首先引入高效视觉Mamba网络（efficient vision Mamba，EfficientVIM）模块，通过状态空间建模与深浅层特征交互，实现全局与局部信息的高效融合，从而增强小目标特征感知与跨尺度建模能力；其次，构建卷积门控线性单元（convolutional gated linear unit，CGLU）模块，利用卷积与门控机制的协同作用动态抑制复杂背景噪声，有效突出鼠洞边缘与纹理特征；最后，设计小目标加权损失函数（CGLU-Loss），在回归阶段引入中心偏移权重与局部重叠增益项，以强化小尺度样本的梯度贡献并提升定位精度。试验基于2 397张跨季节无人机（UAV）草原鼠洞图像数据集开展，结果表明：RT-DETR-ECG在检测精度、实时性与模型轻量化方面均优于现有算法。在IoU阈值为0.5时，模型的平均精度（mAP@0.5）达96.3%，较原始RT-DETR提升5.1%，检测速度达91.8帧/s，计算负载降低53.7%。该模型实现了小目标检测的精度提升与实时性能的平衡，为草原鼠洞智能识别与生态监测提供了一种高效可靠的新方法。
, authors=董振伟, 付学良, 李宏慧, 潘新, 徐喆, 罗小玲
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董振伟，男，1998年生，河南商丘人，硕士研究生；研究方向为网络与信息安全。E-mail：dongzw@emails.imau.edu.cn
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董振伟，男，1998年生，河南商丘人，硕士研究生；研究方向为网络与信息安全。E-mail：dongzw@emails.imau.edu.cn
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