江苏农业学报2026，Vol.42Issue(8)：1584-1596,13.DOI:10.3969/j.issn.1000-4440.2026.08.008
基于YOLO-GLBW模型的复杂田间环境下玉米田杂草检测
Weed detection in maize fields under complex field conditions based on the YOLO-GLBW model
摘要
Abstract
To address the issues of mutual occlusion between maize seedlings and weeds,similar morphology,and dense weed distribution in complex field environments,which lead to difficulties in weed target localization,as well as the problems of insufficient long-range dependency modeling,low detection accuracy,and low inference efficiency in existing detection models,this study proposed an improved model,YOLO-GLBW,based on YOLOv8n.First,we embedded the ghost convolution(GhostConv)structure into the C2f modules of both the backbone feature extraction network and all C2f modules of the neck network,which effectively reduced the model's parameter count and computational cost while preserving its full feature representation capabil-ity.We introduced the large separable kernel attention(LSKA)mechanism into the fast spatial pyramid pooling(SPPF)module,which effectively compensated for the model's deficiency in long-range dependency and enhanced its perception capability for small targets.Second,we adopted a bidirectional feature pyramid network(BiFPN)to improve multi-scale target detection and localization performance.Finally,we introduced the wise intersection over union(WIoU)loss function to optimize localization and classification performance in complex scenarios,accelerate the convergence speed,and enhance the generalization ability.In this experiment,we tested the proposed model on a self-constructed dataset of maize seedling and weed images collected from field environments.The results showed that YOLO-GLBW achieved mAP50 of 83.9%,with 2.47 M parameters and a computational cost of 6.3 GFLOPs(1.0×109 floating-point operations).Compared with the original YOLOv8 model,YOLO-GLBW improved precision,recall,mAP50,and mAP50-95 by 1.4 percentage points,2.1 percentage points,1.6 percentage points,and 0.5 percentage points,respectively,while reducing the parameter count and computa-tional cost by 17.9%and 22.2%,respectively.The YOLO-GLBW model significantly reduced computational cost and achieved a lightweight design,while further improving object detection accuracy.In the face of complex scenarios character-ized by dense distribution,morphological similarity,and mutual occlusion between maize seedlings and weeds,the model maintains stable detection accuracy,resolves the trade-off between recognition precision and efficiency,and demonstrates considerable potential for practical field deployment.关键词
YOLOv8n 模型/玉米田/杂草/目标检测Key words
YOLOv8n model/maize field/weeds/object detection分类
信息技术与安全科学引用本文复制引用
刘希岩,赵秋实,杨锦锋,刘铱凡,隋新..基于YOLO-GLBW模型的复杂田间环境下玉米田杂草检测[J].江苏农业学报,2026,42(8):1584-1596,13.基金项目
黑龙江省自然科学基金项目(LH2023F048) （LH2023F048）
