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一种基于YOLOv8n的轻量级方法,用于检测人类异常姿势
Guilin Li1, Jiarui Zhang2, Qiyuan Ji1
1School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou, China.
Scientific reports
|February 4, 2026
概括
本研究介绍了PSD-YOLOv8n,这是一种轻量级算法,用于检测异常的人体姿势,提高医疗保健和安全应用的准确性和效率. 该模型在紧的尺寸下实现了高性能,为实时监控提供了可靠的解决方案.
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:
- 检测异常的人体姿势对于智能医疗,公共安全和老年护理至关重要.
- 现有的方法面临诸如阻塞,姿势变化,数据不平衡和大模型大小等挑战,阻碍实时性能.
研究的目的:
- 开发一种轻量级且准确的人类异常姿势检测算法.
- 在复杂的场景和资源有限的环境中解决当前方法的局限性.
主要方法:
- 基于YOLOv8n的拟议PSD-YOLOv8n算法,结合了具有空间和通道注意力的PoseMSA模块.
- 利用可分离的卷积和剩余的瓶来有效地提取特征.
- 引入了一个关键点意识的KA-Sample上采样模块和一个Detect-PSA检测头,用于增强的空间关系建模.
主要成果:
- 在SSHDataset上,PSD-YOLOv8n实现了97.8%的mAP@0.5和75.8%的mAP@0.5:0.95.
- 超越了基线YOLOv8n和先进的模型,如YOLOv9-T,YOLOv10n和YOLOv11n.
- 该型号拥有紧的2.07M参数数量和4.5MB的重量尺寸,展示了轻量级,高精度的能力.
结论:
- PSD-YOLOv8n提供了一种高效可靠的解决方案,用于检测人类异常姿势.
- 该算法在复杂的环境和各种姿势中显示出稳健性.
- 轻量级的设计使其适用于智能医疗保健和安全监控中的实时应用.
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