融合Mamba与Transformer，实现毫秒级精准穴位定位
RT-DEMT: A hybrid real-time acupoint detection model combining mamba and transformer
- 用Mamba替代部分注意力机制，提升推理效率
- 在背部穴位数据集上误差仅7.792像素，耗时10.05毫秒
- 适合智能针灸机器人开发，兼顾速度与精度
传统中医针灸因主观性强而存在临床争议。现有智能辅助针灸系统普遍存在定位速度慢、准确率低的问题。为此，本文提出一种结合状态空间模型Mamba与传统DETR架构注意力机制的混合实时穴位检测方法，利用Mamba的高效推理能力，同时保留全局信息整合优势，实现高质量特征提取。通过引入残差似然估计思想，避免复杂上采样过程，显著加速定位任务。在自建的人体背部穴位数据集上，该方法达到当前最优性能，平均欧氏距离像素误差（EPE）为7.792，单次定位耗时10.05毫秒。相比第二优算法，准确率与速度均提升约14%。该成果不仅提升针灸疗效，更展现出自动化针灸机器人系统的商业化潜力。
原文摘要 · Abstract (English)
Traditional Chinese acupuncture methods often face controversy in clinical practice due to their high subjectivity. Additionally, current intelligent-assisted acupuncture systems have two major limitations: slow acupoint localization speed and low accuracy. To address these limitations, a new method leverages the excellent inference efficiency of the state-space model Mamba, while retaining the advantages of the attention mechanism in the traditional DETR architecture, to achieve efficient global information integration and provide high-quality feature information for acupoint localization tasks. Furthermore, by employing the concept of residual likelihood estimation, it eliminates the need for complex upsampling processes, thereby accelerating the acupoint localization task. Our method achieved state-of-the-art (SOTA) accuracy on a private dataset of acupoints on the human back, with an average Euclidean distance pixel error (EPE) of 7.792 and an average time consumption of 10.05 milliseconds per localization task. Compared to the second-best algorithm, our method improved both accuracy and speed by approximately 14\%. This significant advancement not only enhances the efficacy of acupuncture treatment but also demonstrates the commercial potential of automated acupuncture robot systems. Access to our method is available at https://github.com/Sohyu1/RT-DEMT
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