基于YOLO 26n-seg和沙漏神经网络模型的草莓相对轮廓预测OA

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.