农业机械学报2026，Vol.57Issue(12)：264-274,11.DOI:10.6041/j.issn.1000-1298.2026.12.025
基于YOLO 26n-seg和沙漏神经网络模型的草莓相对轮廓预测
Strawberry Relative Contour Prediction Based on YOLO 26n-seg and Hourglass Network Model
摘要
Abstract
Addressing the issue that in unstructured agricultural environments,strawberry fruits are often obscured by leaves,stems,and adjacent fruits,making it difficult for machine vision systems to obtain complete geometric contours,which in turn affects the positioning accuracy and grasping success rate of picking robots,a relative contour cascade prediction method was proposed for strawberries that integrated an improved YOLO 26 instance segmentation with a Hourglass Network.This method first used the latest YOLO 26n-seg model as the visual perception backbone.By introducing a lightweight GhostNetV2 to reconstruct the backbone network and embedding an attention mechanism,it accurately segmented the mask of the fruit's visible area against complex backgrounds.Secondly,to address the challenge of shape inference in occluded areas,principal component analysis(PCA)rotation correction was applied to standardize contour features,and a relative contour prediction model based on the Hourglass Network was constructed.This model leveraged its symmetric bottom-up and top-down structure to effectively integrate local details of the fruit's visible parts with global semantic features,establishing a mapping from visible area features to the coordinates of the entire contour.Experimental results showed that the proposed improved segmentation model achieved an average precision(AP)of 91.3%with parameters reduced to 3×106,and the contour prediction model maintained an average R2 between 0.650 0 and 0.698 7 with a mean absolute error(MAE)of around 9 pixels across different occlusion directions on the test set,significantly outperforming uncorrected and traditional multi-layer perceptron(MLP)models.This method enabled accurate shape completion of strawberry fruits under various degrees of occlusion,providing reliable spatial geometric information for obstacle avoidance planning and non-destructive grasping by intelligent orchard picking robots.关键词
草莓轮廓预测/YOLO 26n-seg/Hourglass Network/实例分割/PCA/旋转校正Key words
strawberry contour prediction/YOLO 26n-seg/Hourglass Network/instance segmentation/PCA/rotation correction分类
信息技术与安全科学引用本文复制引用
洪旗,孔鸣宇,贺磊磊,孔朔琳,杨蜀秦,傅隆生..基于YOLO 26n-seg和沙漏神经网络模型的草莓相对轮廓预测[J].农业机械学报,2026,57(12):264-274,11.基金项目
国家自然科学基金项目(32572201) （32572201）
