集成技术2025，Vol.14Issue(2)：58-70,13.DOI:10.12146/j.issn.2095-3135.20241129001
YOLO-PointMap:基于轻量化动态特征融合的实时人体背部穴位识别
YOLO-PointMap:Real-time Human Back Acupoint Recognition Based on Lightweight Dynamic Feature Fusion
摘要
Abstract
As a vital part of traditional Chinese medicine,acupuncture has extensive application value all over the world.However,the reliance on practitioners'experience for acupoint localization in traditional acupuncture methods leads to a lack of standardization,restricting its reproducibility and broader adoption.Acupuncture robots,as a kind of intelligent medical devices,offer new opportunities for standardizing and promoting acupuncture techniques.This paper introduces an improved YOLOv8-Pose model,YOLO-PointMap,designed to address challenges in dense acupoint distribution and weak feature recognition.By incorporating dynamic convolution to optimize the C2f module and introducing a channel-attention-based feature fusion module,the model achieves significant advancements in multi-scale feature extraction and integration.Experimental results show that the end point error(EPE),percentage of correct keypoints(PCK)and mAP50-95(Pose)indexes of YOLO-PointMap on the test set are superior to the existing models,with the values reaching 3.27,1.00 and 84.90%respectively,especially in dense key point identification and weak feature region localization.It provides strong support for the development of acupuncture robot technology,and shows the potential application value in the fields of virtual reality and intelligent interaction.关键词
穴位定位/关键点检测/YOLO-PointMap/动态卷积/特征融合Key words
acupoint localization/keypoint detection/YOLO-PointMap/dynamic convolution/feature fusion分类
计算机与自动化引用本文复制引用
黄凌风,杨世龙,谢耀钦..YOLO-PointMap:基于轻量化动态特征融合的实时人体背部穴位识别[J].集成技术,2025,14(2):58-70,13.基金项目
国家自然科学基金项目(U20A20373) （U20A20373）
国家科技攻关计划项目(2022YFC2409000) （2022YFC2409000）
深圳市科技计划项目(KQTD20180411185028798,ZDSY20130401165820357) （KQTD20180411185028798,ZDSY20130401165820357）
介入手术机器人诊疗关键技术深圳市工程实验室项目(XMHT20220104009) This work is supported by National Natural Science Foundation of China(U20A20373) （XMHT20220104009）
National Key Research and Development Program of China(2022YFC2409000) （2022YFC2409000）
Shenzhen Science and Technology Program(KQTD20180411185028798,ZDSY20130401165820357) （KQTD20180411185028798,ZDSY20130401165820357）
Shenzhen Engineering Laboratory for Diagnosis&Treatment Key Technologies of Interventional Surgical Robots(XMHT20220104009) （XMHT20220104009）
