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LFD-YOLO:一个轻量级的跌落探测网络,具有增强的特征提取和融合功能

Heqing Wang¹, Sheng Xu², Yuandian Chen³

¹School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China.

Scientific reports

|February 11, 2025

概括

这项研究介绍了轻量级摔倒检测YOLO (LFD-YOLO),这是一种用于老年人摔倒检测的新型模型. LFD-YOLO实现了高精度与降低计算复杂性,使其适合边缘设备.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
老年学是一门学科.

背景情况:

布对老年人口构成重大安全风险.
现有的物体检测模型用于降落检测通常是计算密集型,阻碍其在资源有限的边缘设备上使用.
轻量级模型可能会为了减少计算需求而牺牲精度.

研究的目的:

开发适合边缘设备部署的轻量级和准确的跌落检测模型.
解决老年人落检测系统中计算复杂度和检测准确度之间的权衡问题.
提出一种基于YOLOv5架构的轻量级落检测YOLO (LFD-YOLO) 新型落检测模型.

主要方法:

提出了一种新的轻量级特征提取模块,Cross Split RepGhost (CSRG),以尽量减少信息丢失.
集成高效多尺度注意力 (EMA) 提高了对人类姿势的关注度.
开发了一个加权聚变金字塔网络 (WFPN) 与组混合卷积 (GSConv) 进行高效的多尺度特征聚变和降低复杂性.
引入了内部加权交叉点在联盟 (内部WIoU) 上的损失函数,以提高趋同性和通用性.
创建了一个多样化的个人摔倒检测数据集 (PFDD) 并使用了摔倒姿势图像数据集 (FPID).

主要成果:

与YOLOv5s.相比,LFD-YOLO在PFDD和FPID数据集上的平均精度 (mAP0.5) 提高了1.5%和1.7%,与YOLOv5s.相比.
与YOLOv5s相比,实现了参数减少19.2%,计算减少21.3%.
通过将参数减少48.6%和计算减少56.1%,优于YOLOv8,同时改善mAP0.5的0.3%和0.5%.
该模型表现出更高的检测精度和更低的计算复杂性.

结论:

LFD-YOLO为老年人摔倒检测提供了一个有前途的解决方案,平衡准确性和效率.
拟议的轻量级架构和新型模块对于在资源有限的边缘设备上部署是有效的.
这项研究有助于通过先进的人工智能技术改善老年人的安全和福祉.

关键词:

落检测落检测落检测功能提取功能提取功能融合的特点是:在内海的损失.对象检测检测对象检测对象检测

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