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一种基于修改后的YOLOv8s和AlphaPose的混合人类摔倒检测方法
Lei Liu1,2, Yeguo Sun3, Yinyin Li2
1Human-Computer Collaborative Robot Joint Laboratory of Anhui Province, Huainan, China.
Scientific reports
|January 21, 2025
概括
本研究介绍了HFDMIA-Pose,这是一种混合式落检测系统,可以提高小物体和多人场景的精度. 该方法提高了检测速度,并减少了模型大小,使其适用于边缘设备.
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:
- 现有的落检测系统在小物体准确性和多人场景方面扎.
- 在现实应用中需要有效和准确的人类落检测.
研究的目的:
- 开发一种混合落检测方法 (HFDMIA-Pose),解决当前系统的局限性.
- 为了提高准确性,减少模型大小,并提高多人跌倒识别的适用性.
主要方法:
- 使用了经过修改的YOLOv8s物体探测器,配备了SPD-Conv和一个小物体探测层,采用了BCIOU损失.
- 集成了使用人类骨节点的混合算法,以提高跌倒识别精度.
- 开发了一个多人跌倒检测数据集 (MPFDD) 用于全面测试.
主要成果:
- 与AlphaPose相比,HFDMIA-Pose的精度 (4.30%) 和F1得分 (4.57%) 提高了,FPS增加了37.50%.
- 与其他模型相比,在精度方面实现了5.33%的平均改进,在F1得分方面提高了5.51%.
- 展示了显著的性能提升,缩小模型大小和更快的处理速度.
结论:
- HFDMIA-Pose为人类落检测提供了卓越的性能,准确性和效率.
- 该方法的优点使其非常适合资源有限的边缘环境和各种日常场景.
- 这种混合方法为先进的人类落检测提供了具有竞争力和有效的解决方案.
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