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整合注意力机制和多尺度特征提取用于落检测
Hao Chen1, Wenye Gu2, Qiong Zhang1
1School of Computer and Information Engineering, Nantong Institute of Technology, China.
Heliyon
|June 4, 2024
概括
本研究介绍了SCPE-YOLOv5s模型,用于准确检测落事件. 改进的模型改进了特征提取,以提高检测准确度和速度,为这一关键安全问题提供了新的解决方案.
科学领域:
- 计算机科学 计算机科学
- 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:
- 秋季事件对健康构成重大风险,需要准确及时检测.
- 现有的落检测方法难以应对不同的落尺度和微妙的姿势变化.
- 你只看一次版本5小 (YOLOv5s) 模型是对象检测的基础,但需要增强用于跌倒检测.
研究的目的:
- 引入一种新的落事件检测模型SCPE-YOLOv5s,旨在提高准确性和效率.
- 增强功能提取能力,用于检测各种尺度和姿势的落.
- 为了提供一个强大的解决方案,用于现实世界的下降事件监控.
主要方法:
- SCPE-YOLOv5s模型将空间注意力与高效通道注意力 (ECA) 网络集成在一起,以改善空间姿势特征提取.
- 平均聚合层被纳入空间金字塔聚合 (SPP) 网络,以支持多层次的摔倒姿势检测.
- ECA网络与SPP相结合,有效地将全球和本地特征合并,以增强特征表示.
主要成果:
- 在公开数据集上,SCPE-YOLOv5s模型实现了88.29%的平均平均精度 (mAP).
- 与标准YOLOv5s模型相比,这代表了mAP的4.87%的改善.
- 该模型显示了每秒57.4的处理速度.
结论:
- SCPE-YOLOv5s模型在降落事件检测准确性和效率方面取得了重大进展.
- 整合空间注意力和增强的聚合策略有效地解决了在检测微妙和多层次的跌倒姿势方面的挑战.
- SCPE-YOLOv5s为关键落检测应用提供了一个有前途和新的解决方案.
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