智能化农业装备学报（中英文）2026，Vol.7Issue(1)：63-74,12.DOI:10.12398/j.issn.2096-7217.2026.01.007
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
Design and experiment of an intelligent recognition and detection model for grassland rodent burrows based on improved RT-DETR
摘要
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.关键词
RT-DETR-ECG/目标检测/深度学习/图像识别/无人机影像/啮齿动物洞穴Key words
RT-DETR-ECG/object detection/deep learning/image recognition/UAV imagery/rodent burrow分类
农业科技引用本文复制引用
董振伟,付学良,李宏慧,潘新,徐喆,罗小玲..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[J].智能化农业装备学报（中英文）,2026,7(1):63-74,12.基金项目
高层次及优秀博士人才引进科研启动项目(NDYB2022-60) （NDYB2022-60）
内蒙古自治区自然科学基金项目(2023LHMS06020) （2023LHMS06020）
内蒙古自然基金重点项目(2025ZD012) （2025ZD012）
内蒙古自治区揭榜挂帅项目(2025KJTW0026) （2025KJTW0026）
内蒙古自治区重点项目(2025KYPT0076) High-level and Excellent Doctoral Talent Introduction Scientific Research Start-up Project(NDYB2022-60) （2025KYPT0076）
Natural Science Foundation of Inner Mongolia Autonomous Region(2023LHMS06020) （2023LHMS06020）
Key Project of Natural Sci-ence Foundation of Inner Mongolia(2025ZD012) （2025ZD012）
"Revealing the List and Taking the Lead"Project of Inner Mongolia Autonomous Region(2025KJTW0026) （2025KJTW0026）
Key Science and Technology Project of Inner Mongolia Autonomous Region(2025KYPT0076) （2025KYPT0076）
