农业机械学报2025，Vol.56Issue(5)：405-414,10.DOI:10.6041/j.issn.1000-1298.2025.05.038
基于改进YOLO v8n的非结构环境下杭白菊检测方法
Improved YOLO v8n for Detection of Hangzhou White Chrysanthemum in Unstructured Environments
摘要
Abstract
In unstructured environments,the cluster growth characteristics of Hangzhou white chrysanthemum lead to severe mutual occlusion,reducing detection accuracy for chrysanthemum detection algorithms.To address this issue,an improved YOLO v8n detection model for Hangzhou white chrysanthemum,called Hangzhou white chrysanthemum-YOLO v8n(Hwc-YOLO v8n),was proposed.Firstly,the model's ability was enhanced to finely detect critical,similar features of the chrysanthemum by increasing the label categories from two to three.Secondly,a dynamic feature extraction module(C2f-Dynamic)was designed in the backbone network to strengthen the model's adaptive response to missing features in occluded targets.Additionally,a 160 pixel × 160 pixel detection head was added to the detection head section,allowing the model to detect small targets more effectively.Finally,the angle penalty metric loss(SIoU)was adopted to optimize the bounding box loss function,improving both detection accuracy and generalization capability.Experimental results from module placement and heatmap analysis demonstrated that the C2f-Dynamic module can dynamically adapt to feature changes in occluded targets.The improved Hwc-YOLO v8n model achieved a 1.7 percentage points increase in mean average precision and a 0.88 percentage points increase in mean recall rate for the occluded Hangzhou white chrysanthemum.Ablation and comparison experiments showed that the improved Hwc-YOLO v8n outperformed DETR,SSD,YOLO v5,YOLO v6,and YOLO v7 in detection of the chrysanthemum.Specifically,compared with DETR,SSD,YOLO v5,YOLO v6,and YOLO v7,the mAP was improved by 5.7,12.6,0.7,0.75,and 11.25 percentage points,respectively.The mR was increased by 2.15 percentage points and 1.4 percentage points compared with that of YOLO v5 and YOLO v7,respectively.The research result can provide a technical foundation for future intelligent harvesting of Hangzhou white chrysanthemum.关键词
杭白菊/图像识别/目标检测/YOLO v8/遮挡检测Key words
Hangzhou white chrysanthemum/image recognition/object detection/YOLO v8/occlusion detection分类
信息技术与安全科学引用本文复制引用
喻陈楠,伍永红,周杰,姚坤,郇晓龙,陈建能..基于改进YOLO v8n的非结构环境下杭白菊检测方法[J].农业机械学报,2025,56(5):405-414,10.基金项目
国家自然科学基金项目(32301715、U23A20175)、全省农业智能感知与机器人重点实验室开放课题基金项目(2025QSZD2505)和浙江理工大学校内科研启动基金项目(23242167-Y) （32301715、U23A20175）
