湖北农业科学2025，Vol.64Issue(8)：1-9,9.DOI:10.14088/j.cnki.issn0439-8114.2025.08.001
基于改进RT-DETR模型的油菜田间杂草识别研究
Research on rapeseed field weed recognition based on improved RT-DETR model
摘要
Abstract
Four typical weeds Xanthium strumarium,Setaria viridis,Chenopodium album,Ambrosia artemisiifoliain in rapeseed fields were taken as the research objects.Key challenges in weed detection,including small seedling targets,weak features of withered weeds,and difficulty in identifying highly overlapping areas,were addressed by proposing an improved detection method based on the RT-DETR(Region transformer DETR)model.The asymptotic feature pyramid network(AFPN)replaced the original cross-scale con-text fusion module(CCFM)in the RT-DETR model,effectively resolving the imbalanced feature distribution issue in with-ered weeds caused by blurred texture and feature sparsity.The SPD-Conv module was introduced into the backbone network to en-hance the feature representation capability for small-target weeds.The convolutional block attention module(CBAM)was integrated at the end of the backbone network,effectively mitigating feature information loss under low-resolution targets and occlusion condi-tions.Systematic ablation experiments and comparative experiments verified that the improved RT-DETR+AFPN+SPD-Conv+CBAM(RW-DETR)model demonstrated significant advantages in both detection accuracy and robustness.The RW-DETR model achieved recognition precision and mean average precision of 85.2%and 82.5%,respectively,for weeds in rapeseed fields,significantly outper-forming the RT-DETR model,Faster R-CNN model,SSD model,YOLOv5m model,and YOLOv8m model.While maintaining real-time detection performance,the RW-DETR model significantly improved weed recognition effectiveness in complex scenes,meeting the accuracy and efficiency requirements of modern agriculture for weed detection systems.关键词
油菜/改进RT-DETR模型/RT-DETR模型/杂草识别Key words
rapeseed/improved RT-DETR model/RT-DETR model/weed recognition分类
信息技术与安全科学引用本文复制引用
章磊,冷欣,陈佳凯,李宗轩..基于改进RT-DETR模型的油菜田间杂草识别研究[J].湖北农业科学,2025,64(8):1-9,9.基金项目
国家自然科学基金项目(62301139) （62301139）
