森林工程2026，Vol.42Issue(5)：1022-1032,11.DOI:10.7525/j.issn.1006-8023.2026.05.013
基于改进YOLOv11n-seg的木椅腿实例分割方法
Instance Segmentation Method for Wooden Chair Legs Based on Improved YOLOv11n-seg
摘要
Abstract
Accurate identification of wooden chair legs and localization of grasping positions are crucial for achieving effi-cient robotic loading operations.However,existing instance segmentation-based image processing algorithms often struggle to balance accuracy and speed,and their large number of parameters hinders practical deployment in industrial scenarios.Furthermore,these algorithms are prone to missed detections and false detections in complex environments.To address the aforementioned issues,this paper proposes an improved YOLOv11n-seg model.By incorporating light-weight modules and a novel loss function,the model achieves a favorable balance between accuracy and efficiency,thereby optimizing its deployment performance.The specific improvements are as follows:firstly,RepViT is introduced as the backbone network to enhance feature extraction capability while reducing computational complexity.Secondly,the lightweight dynamic upsampler DySample is adopted in the neck network to improve feature fusion efficiency.Fi-nally,Shape-IoU is used to replace the traditional CIoU Loss loss function,optimizing the regression accuracy of bound-ing boxes.Results of ablation experiments and comparative experiments demonstrate that the improved YOLOv11n-seg algorithm outperforms the original model in both inference speed and segmentation accuracy.Specifically,the total num-ber of model parameters is reduced by 11.31%,the floating-point operations(FLOPs)are decreased by 9.62%,and the mean average precision for segmentation(mAP@0.5:0.95)is increased by 4.61 percentage points.These findings vali-date the effectiveness of the improved algorithm and provide technical support for the subsequent precise positioning and grasping of wooden chair legs by robots.关键词
木椅腿加工/实例分割/YOLO/轻量化/深度学习Key words
Wooden chair leg machining/instance segmentation/YOLO/lightweight/deep learning分类
农业科技引用本文复制引用
徐冀策,王海艳,王宁,魏泽辉,吴哲..基于改进YOLOv11n-seg的木椅腿实例分割方法[J].森林工程,2026,42(5):1022-1032,11.基金项目
国家重点研发计划项目(2024YFD2200700). （2024YFD2200700）
