YOLOv8-ACU: improved YOLOv8-pose for facial acupoint detection

IF 2.6 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Frontiers in Neurorobotics Pub Date : 2024-01-17 DOI:10.3389/fnbot.2024.1355857

Zijian Yuan, Pengwei Shao, Jinran Li, Yinuo Wang, Zixuan Zhu, Weijie Qiu, Buqun Chen, Yan Tang, Aiqing Han

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Abstract

Introduction

Acupoint localization is integral to Traditional Chinese Medicine (TCM) acupuncture diagnosis and treatment. Employing intelligent detection models for recognizing facial acupoints can substantially enhance localization accuracy.

Methods

This study introduces an advancement in the YOLOv8-pose keypoint detection algorithm, tailored for facial acupoints, and named YOLOv8-ACU. This model enhances acupoint feature extraction by integrating ECA attention, replaces the original neck module with a lighter Slim-neck module, and improves the loss function for GIoU.

Results

The YOLOv8-ACU model achieves impressive accuracy, with an mAP@0.5 of 97.5% and an mAP@0.5–0.95 of 76.9% on our self-constructed datasets. It also marks a reduction in model parameters by 0.44M, model size by 0.82 MB, and GFLOPs by 9.3%.

Discussion

With its enhanced recognition accuracy and efficiency, along with good generalization ability, YOLOv8-ACU provides significant reference value for facial acupoint localization and detection. This is particularly beneficial for Chinese medicine practitioners engaged in facial acupoint research and intelligent detection.

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YOLOv8-ACU：用于面部穴位检测的改进型 YOLOv8-pose

引言穴位定位是中医针灸诊断和治疗不可或缺的一部分。本研究介绍了 YOLOv8 姿态关键点检测算法的改进版，该算法专为面部穴位量身定制，并命名为 YOLOv8-ACU。该模型通过整合 ECA 注意加强了穴位特征提取，用更轻的 Slim-neck 模块取代了原来的颈部模块，并改进了 GIoU 的损失函数。结果 YOLOv8-ACU 模型取得了令人印象深刻的准确性，在我们自建的数据集上，其 mAP@0.5 的准确率为 97.5%，mAP@0.5-0.95 的准确率为 76.9%。讨论YOLOv8-ACU提高了识别精度和效率，并具有良好的泛化能力，为面部穴位定位和检测提供了重要的参考价值。这对从事面部穴位研究和智能检测的中医工作者尤其有益。

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来源期刊

Frontiers in Neurorobotics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCER-ROBOTICS

CiteScore

5.20

自引率

6.50%

发文量

250

审稿时长

14 weeks

期刊介绍： Frontiers in Neurorobotics publishes rigorously peer-reviewed research in the science and technology of embodied autonomous neural systems. Specialty Chief Editors Alois C. Knoll and Florian Röhrbein at the Technische Universität München are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neural systems include brain-inspired algorithms (e.g. connectionist networks), computational models of biological neural networks (e.g. artificial spiking neural nets, large-scale simulations of neural microcircuits) and actual biological systems (e.g. in vivo and in vitro neural nets). The focus of the journal is the embodiment of such neural systems in artificial software and hardware devices, machines, robots or any other form of physical actuation. This also includes prosthetic devices, brain machine interfaces, wearable systems, micro-machines, furniture, home appliances, as well as systems for managing micro and macro infrastructures. Frontiers in Neurorobotics also aims to publish radically new tools and methods to study plasticity and development of autonomous self-learning systems that are capable of acquiring knowledge in an open-ended manner. Models complemented with experimental studies revealing self-organizing principles of embodied neural systems are welcome. Our journal also publishes on the micro and macro engineering and mechatronics of robotic devices driven by neural systems, as well as studies on the impact that such systems will have on our daily life.