Article(id=1319291363443716262, tenantId=1146029695717560320, journalId=1255847919539208197, issueId=1319291312826863771, articleNumber=null, orderNo=null, doi=10.13193/j.issn.1673-7717.2026.08.003, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1792473172293, onlineDateStr=2026-10-20, pubDate=1786291200000, pubDateStr=2026-08-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792473172293, onlineIssueDateStr=2026-10-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792473172293, creator=13701087609, updateTime=1792473172293, updator=13701087609, issue=Issue{id=1319291312826863771, tenantId=1146029695717560320, journalId=1255847919539208197, year='2026', volume='44', issue='8', pageStart='1', pageEnd='258', issueExtLink='null', onlineDate='null', pubDate='1786291200000', pubDateStr='2026-08-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792473160225, creator='13701087609', updateTime=1792473382588, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1319292245547799195, tenantId=1146029695717560320, journalId=1255847919539208197, issueId=1319291312826863771, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1319292245652656796, tenantId=1146029695717560320, journalId=1255847919539208197, issueId=1319291312826863771, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=12, endPage=18, ext={EN=ArticleExt(id=1319291365012385959, articleId=1319291363443716262, tenantId=1146029695717560320, journalId=1255847919539208197, language=EN, title=Research on YOLOv12n-BiGS Traditional Chinese Medicine Auricular Point Detection Algorithm Based on Auricular Point Partition, columnId=1256183375241535778, journalTitle=Chinese Archives of Traditional Chinese Medicine, columnName=Digital Traditional Chinese Medicine, runingTitle=null, highlight=null, articleAbstract=

As the body surface mapping area of human organs and meridians, the accurate identification and location of auricular points in traditional Chinese medicine is the key technical support for auricular acupuncture diagnosis and treatment, traditional Chinese medicine health monitoring and other scenes. Traditional ear acupoint detection is highly dependent on the clinical experience of professional doctors, which is not only subjective and inefficient, but also difficult to meet the needs of standardized application. The existing target detection algorithms generally have pain points that are difficult to balance accuracy and speed in auricular point detection. To this end, this study proposes an improved traditional Chinese medicine auricular point detection algorithm based on YOLOv12. First of all, combined with the national standard of auricular points and 9 kinds of TCM diseases, a special data set containing 31 auricular points partitions was constructed, including 2140 auricular points images covering different age groups, ear shape characteristics and shooting environment. In terms of model optimization, the C3k2-GV3S Lightweight Residual Feature Aggregation Module is introduced to replace the C3k2 module of the original backbone network. With the help of the two-stage strategy of “basic feature generation + cheap phantom feature completion”, the parameter quantity and calculation amount are reduced by about 50% on the premise of retaining the advantage of residual feature multiplexing, and the key feature representation of small auricular targets is strengthened. The bilinear adaptive boundary-enhanced upsampling module BABEU (Bilinear Adaptive Boundary-Enhanced Upsampling Module) is designed to replace the traditional interpolation operation. Through the combination strategy of “bilinear interpolation smoothing feature + learnable convolution thinning boundary”, the high-frequency detail features of the auricular point boundary are adaptively learned to avoid pixel block effect and checkerboard artifacts, and the boundary feature recovery accuracy of the auricular point small target is significantly improved. Finally, a bilinear adaptive boundary enhanced upsampling and lightweight residual feature aggregation network YOLOv12n-BiGS (Bilinear Adaptive Boundary-Enhanced Upsampling and Lightweight Residual Feature Aggregation Network) based on YOLOv12n has been constructed. The experimental results show that the average Dice coefficient of the model on the dedicated data set is 0.5821, and the reasoning time is only 3.3 ms. The comprehensive performance is better than the mainstream lightweight algorithms such as YOLOv8 n, YOLOv11 n and DeepEar. The special data set constructed in this study meets the clinical needs, and the improved model realizes the collaborative optimization of auricular point detection accuracy and real-time performance, which provides technical support for the intelligent and standardized promotion of auricular point diagnosis and treatment in traditional Chinese medicine.

, authors=Shujian XIA¹, Weijie ZHOU¹, Weitao YAO¹, Qin JING¹, Ziyi ZHOU¹, Lijian PANG², Yuying QIN¹, Xiaodong LYU¹, Linlin WANG², authorsList=Shujian XIA, Weijie ZHOU, Weitao YAO, Qin JING, Ziyi ZHOU, Lijian PANG, Yuying QIN, Xiaodong LYU, Linlin WANG, authorCompany=null, correspAuthors=Lijian PANG, Linlin WANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1319291365436010664, articleId=1319291363443716262, tenantId=1146029695717560320, journalId=1255847919539208197, language=CN, title=基于耳穴分区的YOLOv12n-BiGS中医耳穴检测算法研究, columnId=1256183387140776248, journalTitle=中华中医药学刊, columnName=数字中医中药, runingTitle=null, highlight=null, articleAbstract=

中医耳穴作为人体脏腑经络的体表映射区域，其精准识别与定位是耳针诊疗、中医健康监测等场景的关键技术支撑。传统耳穴检测高度依赖专业医师的临床经验，不仅主观性强、效率低下，且难以满足标准化应用需求；现有目标检测算法在耳穴检测中普遍存在精度与速度难以兼顾的痛点。为此，提出一种基于YOLOv12的改进型中医耳穴检测算法。首先，结合耳穴国标与9种中医病症，构建包含31个耳穴分区的专用数据集，收录2140张涵盖不同年龄段、耳型特征及拍摄环境的耳穴图像。在模型优化方面，引入C3k2-GV3S轻量残差特征聚合模块（Lightweight residual feature aggregation module）替换原始主干网络的C3k2模块，借助“基础特征生成+廉价幻影特征补全”的双阶段策略，在保留残差特征复用优势的前提下，将参数量与计算量降低约50%，同时强化耳穴小目标的关键特征表征；设计双线性自适应边界增强上采样模块BABEU（Bilinear adaptive boundary-enhanced upsampling module）替代传统插值操作，通过“双线性插值平滑特征+可学习卷积细化边界”的组合策略，自适应学习耳穴边界的高频细节特征，避免像素块效应与棋盘格伪影，显著提升耳穴小目标的边界特征恢复精度。最终构建基于YOLOv12n的双线性自适应边界增强上采样与轻量残差特征聚合网络YOLOv12n-BiGS（Bilinear adaptive boundary-enhanced upsampling and lightweight residual feature aggregation network）。实验结果表明，该模型在专用数据集上的平均Dice系数达0.582 1，推理时间仅3.3 ms，综合性能优于YOLOv8n、YOLOv11n、DeepEar等主流轻量化算法。本研究构建的专用数据集贴合临床需求，改进模型实现了耳穴检测精度与实时性的协同优化，为中医耳穴诊疗的智能化、标准化推广提供技术支撑。

, authors=夏书剑¹, 周伟杰¹, 姚伟涛¹, 荆秦¹, 周子艺¹, 庞立健², 秦钰莹¹, 吕晓东¹, 王琳琳², authorsList=夏书剑, 周伟杰, 姚伟涛, 荆秦, 周子艺, 庞立健, 秦钰莹, 吕晓东, 王琳琳, authorCompany=null, correspAuthors=庞立健, 王琳琳, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=xe5DQhbtypQnUAdU7nk6lw==, magXml=uuHkCAacBAhjXQq16Ae9Ww==, pdfUrl=null, pdf=4/9Qj9TeKYh9yqYxK1s6GQ==, pdfFileSize=3955041, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=Y4uFKk2NDgRAap8DjJRQSg==, mapNumber=null, fund=null)}, authors=[Author(id=1319291365851246768, tenantId=1146029695717560320, journalId=1255847919539208197, articleId=1319291363443716262, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1319291365914161330, tenantId=1146029695717560320, journalId=1255847919539208197, articleId=1319291363443716262, authorId=1319291365851246768, language=EN, stringName=Shujian XIA, firstName=Shujian, middleName=null, lastName=XIA, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.Liaoning University of Traditional Chinese Medicine, Shenyang 110847, Liaoning, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1319291366006436019, tenantId=1146029695717560320, journalId=1255847919539208197, articleId=1319291363443716262, authorId=1319291365851246768, language=CN, stringName=夏书剑, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.辽宁中医药大学，辽宁　沈阳　110847, bio={"content":"

夏书剑（1984-），女，辽宁沈阳人，讲师，硕士，研究方向：中医药人工智能与大数据分析。

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夏书剑（1984-），女，辽宁沈阳人，讲师，硕士，研究方向：中医药人工智能与大数据分析。

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