一个受害者风险识别模型,用于自然引起的城市灾害应急响应
Weipeng Fang1,2,3, Genserik Reniers3,4,5, Dan Zhou2
1School of Economics and Management, Tongji University, Shanghai, China.
Risk analysis : an official publication of the Society for Risk Analysis
|September 14, 2024
概括
本研究介绍了一种使用YOLOv7-W6算法的人工智能模型,用于在城市灾害中快速,准确地识别受害者. 它优先考虑"摔倒"姿势,以改善紧急救援决策.
科学领域:
- 灾害管理 灾害管理
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 中国的城市灾害带来了重大风险.
- 目前的灾后评估缺乏实时准确性,阻碍了救援工作.
研究的目的:
- 为了提高实时受害者风险识别的准确性和速度.
- 开发一个智能模型,用于现场评估灾难受害者.
主要方法:
- 使用你只看一次v7-W6 (YOLOv7-W6) 算法进行对象检测.
- 定义的"跌倒"作为紧急受害者识别的关键指标.
- 建立了一个灾难受害者数据库,用于模型培训和验证.
主要成果:
- 在受害者识别方面取得了卓越的准确性 (mAP@0.5,0.960).
- 证明了高推断速度 (5.1毫秒),可实现实时应用.
- 性能优于现有的最先进的物体检测算法.
结论:
- 基于YOLOv7-W6的模型为智能现场受害者风险识别提供了有效的解决方案.
- 这种方法有助于政府的紧急决策和应对.
- 该模型的实际实用性通过真实的灾难救援案例研究得到了验证.
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