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基于YOLOv8n的各种图像退化场景中用于行人跌倒检测的高效算法
Jianhui Xun1, Xuefeng Wang2, Xiufang Wang3
1Department of Electronic Information Engineering, Jining Polytechnic, Jining, 272000, China. xunjh_sci@163.com.
Scientific reports
|March 17, 2025
概括
这项研究引入了PCE-YOLO,这是一种先进的算法,用于检测老人行人跌倒,提高了在具有挑战性的条件下的准确性. 增强的系统为弱势群体提供可靠的实时摔倒检测.
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:
- 越来越多的老年人口需要强大的行人跌倒检测系统.
- 目前的系统在雨,雪或低光等不利条件下难以准确.
- 城市环境对可靠的跌倒检测提出了独特的挑战.
研究的目的:
- 开发一个增强的行人跌倒检测算法,PCE-YOLO,以提高准确性和性能.
- 在复杂和退化的环境条件下解决现有系统的局限性.
- 为老年人跌倒检测提供实时解决方案.
主要方法:
- 基于YOLOv8n的拟议PCE-YOLO算法,包含一个思维链促成自适应增强器 (CPA-Enhancer) 模块.
- 优化跨阶段部分瓶与2卷积块 (C2f) 减少计算负载.
- 实施的内部扩展交叉点在联盟 (内部-EIoU) 损失上进行增强的边界框回归.
- 通过使用7,782张行人落图像和三个退化数据集的数据集进行验证.
主要成果:
- 在原始和退化的数据集上,PCE-YOLO在平均平均精度 (mAP) 上比YOLOv8n提高了4.52%.
- 该算法展示了高处理速度,每秒210.5 (FPS).
- 在各种具有挑战性的环境中显著提高了检测准确度和速度.
结论:
- PCE-YOLO提供了一种强大而准确的解决方案,用于实时检测行人跌倒,特别适用于老年人.
- 该算法的在恶化条件下的性能使其适合于现实世界的应用.
- PCE-YOLO代表了计算机视觉在公共安全和医疗保健方面的重大进步.
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