农业机械学报2026，Vol.57Issue(14)：267-277,11.DOI:10.6041/j.issn.1000-1298.2026.14.025
基于自适应卷积注意力机制的茶叶嫩芽分割与采摘点定位方法
Selective Kernel Attention Mechanism-based Method for Tea Buds Segmentation and Picking Point Localization
摘要
Abstract
Aiming to address the challenges of recognition and localization caused by the dense distribution,multi-scale features,and small target characteristics of tea buds in natural environments,a tea bud instance segmentation model that incorporated an improved attention mechanism was proposed.Additionally,a contour bottom search algorithm(CBS)was employed to accurately locate the picking points of tea buds based on the analysis of the contour's lowest point.Firstly,an improved selective kernel attention mechanism(SKAM)was introduced into the backbone network of YOLO v8n-seg.The spatial pyramid pooling-fast(SPPF)module was replaced with the receptive field block(RFB)module.By expanding the receptive field,the model's ability to capture multi-scale features of tea buds was enhanced.Secondly,a shallow feature fusion structure was designed in the neck network.The up-sampling by dynamic sampling(Dysample)operator was adopted to preserve the details of small targets,thereby improving the segmentation accuracy of densely distributed tea buds.Finally,a lowest point localization algorithm was proposed based on the analysis of the mask contour.The experimental results showed that the bounding box detection indicators of the proposed model(precision of 93.9%,recall of 87.2%,and mAP50 of 93.6%)were improved by 3.7,6.5,and 4.0 percentage points,respectively compared with that of the baseline model.The mask segmentation indicators(90.9%,84.1%,and 88.6%)were correspondingly improved by 4.9,7.4,and 6.0 percentage points.In scenarios with dense distribution and varying scales,the average localization accuracy of the proposed tea bud picking point localization algorithm reached 76.4%.This verified that the method can stably identify dense tea buds of multiple scales and accurately locate the picking points,providing a theoretical basis for the development of intelligent tea picking equipment.关键词
茶叶嫩芽/实例分割/采摘点定位/自然环境/YOLO v8Key words
tea buds/instance segmentation/picking point location/natural environment/YOLO v8分类
信息技术与安全科学引用本文复制引用
杨长辉,张垚鑫,张尧尧,程露,徐春雨,刘彧希..基于自适应卷积注意力机制的茶叶嫩芽分割与采摘点定位方法[J].农业机械学报,2026,57(14):267-277,11.基金项目
国家自然科学基金项目(52075062)、重庆市技术创新与应用发展项目(cstc2021jscx-gksbX0039)和重庆理工大学校级创新项目(gzlcx20252014) （52075062）
