SDM-YOLO11n:一种轻量级,高精度的输液监测方法
Xiangyu Deng1, Wenbo Dong1, Zhecong Fan1
1College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, People's Republic of China.
Biomedical physics & engineering express
|October 7, 2025
概括
一个新的算法,SDM-YOLO11n,通过提高对剩余液体体积的物体检测精度来增强输液监测. 这种轻量级模型在复杂的临床环境中提供精确的实时跟踪.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 生物医学工程 生物医学工程
背景情况:
- 目前的输液监测方法在准确性和复杂性方面存在局限性.
- 非视觉传感器很难安装,容易受到干扰.
- 视觉传感器在复杂的环境中 (如低对比度或遮) 难以准确.
研究的目的:
- 开发一种高精度,轻量级的物体检测算法,用于精确的输液监测.
- 改进输液瓶中的液体的实时检测和体积估计.
- 克服现有的视觉和非视觉传感器技术的局限性.
主要方法:
- 提出了SDM-YOLO11n,这是一个改进的YOLO11n算法.
- 引入了分割并行卷积 (SPConv) 功能提取的增强.
- 实现的动态采样 (DySample) 适应性特征重建和多尺度融合.
- 纳入混合本地频道关注 (MLCA) 以专注于关键地区.
- 开发了一种几何参数比率方法,用于精确的体积估计.
主要成果:
- 与YOLO11n相比,SDM-YOLO11n在mAP@0.5:0.95中取得了0.6个百分点的改善.
- 该模型尺寸为轻量级的5.1 MB.
- 在复杂的场景中证明了输液瓶和液体的高精度检测.
- 启用了对多个输液瓶的实时监控.
结论:
- SDM-YOLO11n在输液监测技术方面取得了重大进展.
- 该算法提供了准确和有效的实时跟踪剩余的液体体积.
- 这种方法适用于复杂的临床环境,改善了患者的护理和安全.
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