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基于改进的YOLOv8模型的婴儿落检测
Ziqian Yang1,2, Baiyu Tsui1,2, Jiachuan Ning3
1College of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, China.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
一个新的实时降落警报系统用于幼儿使用先进的人工智能来检测从高处落. 该系统通过提供及时警报,提高儿童安全,防止严重的健康并发症.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 儿童安全技术 儿童安全技术
背景情况:
- 幼儿从高处落会给他们的健康带来重大风险.
- 现有的监控系统缺乏针对幼儿的特殊摔倒检测.
研究的目的:
- 开发和评估一个实时的幼儿摔倒警报系统.
- 改善幼儿倒后的早期检测和救援.
主要方法:
- 采用GELAN集成的YOLOv8进行身体和头部特征提取的两阶段方法.
- 一般化Hough转换 (GHT) 用于计算"安全区"并分析空间位置.
- 将空间数据与身体 (K) 和头部 (H) 质量中心进行比较,以检测位置状态.
主要成果:
- 与YOLOv3,v5,v6和v8.8相比,提出的框架显示出更高的精度,回忆,平均平均精度和F1分数.
- 在检测幼儿跌倒时,获得了96.33%的准确性.
- 满足了实时监控的每秒 (FPS) 要求.
结论:
- 开发的幼儿目标提取和落下警报系统非常准确和高效.
- 这项技术在主动监测儿童安全方面取得了重大进展.
- 该系统提供了一种可靠的解决方案,用于在家庭环境中实时检测落.
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