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使用增强的人力资源网络结合YOLO的落检测算法.

Huan Shi1, Xiaopeng Wang1, Jia Shi2

  • 1School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.

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概括
此摘要是机器生成的。

这项研究引入了使用YOLOv8和BAM-HRNet的改进的摔倒检测算法,提高了隐蔽场景的准确性. 这种新方法有效地将跌倒与正常活动区分开来,准确度超过95%.

关键词:
这就是YOLOv8的意义.落检测系统 落检测系统 落检测系统高分辨率网络的高分辨率网络骨架的关键点 骨架的关键点

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 生物医学工程 生物医学工程

背景情况:

  • 传统的跌落检测算法与封闭场景,单一跌落判断和实时性能作斗争.
  • 限制包括不足的特征提取和依赖简单的检测方法.

研究的目的:

  • 开发一个强大的,自上而下的摔倒检测算法,克服现有方法的局限性.
  • 提高准确性和实时性能,特别是在具有挑战性的封闭环境中.

主要方法:

  • 用于YOLOv8的轻量级Shufflenetv2骨干,结合了混合注意力机制,以增强人类姿势信息.
  • 集成的BAM-HRNet带有通道注意力,用于精确的关键点提取.
  • 开发了一种多因素的区分基础,包括质量中心速度,干部-地面角速度和身体高度与宽度的比率.
  • 实施了用于摔倒验证的自动语音查询机制.

主要成果:

  • 对象检测模块在COCO上达到64.1%的准确性,在Pascal VOC数据集上达到61.7%.
  • 关键点检测模块在COCO上达到73.49%的准确性,在OCHuman数据集上达到70.11%.
  • 拟议的落检测算法在落检测数据集上超过95%的准确性,率为18.1fps.

结论:

  • 改进的YOLOv8与BAM-HRNet相结合,显著提高了落检测准确度和实时性能.
  • 多因素分析和语音查询机制提高了跌倒识别的可靠性,超过了传统算法.