使用YOLOv8深度学习模型评估邻里身体障碍的协议
STAR protocols
|December 17, 2023
概括
这项研究引入了一种新的方法来测量使用街景和人工智能测量邻里身体障碍 (PD). 这种定量评估有助于了解城市环境质量及其对公共健康的影响.
科学领域:
- 城市规划和公共卫生
- 环境科学环境科学
- 计算机视觉和人工智能的人工智能
背景情况:
- 邻居身体障碍 (PD) 与不利的经济,社会和公共卫生结果有关.
- 评估PD的现有方法缺乏定量标准化和可扩展性.
- 了解和量化PD对于有针对性的城市干预至关重要.
研究的目的:
- 提出一个标准化的协议,用于对邻居身体障碍 (PD) 的定量评估.
- 建立一个可复制的方法来测量PD使用深度学习和街道层面的图像.
- 为PD的跨区域和国际比较提供基础.
主要方法:
- 在不同的城市环境中收集街景图像.
- 开发和实施物体检测模型,特别是YOLOv8,用于识别PD指标.
- 根据检测到的元素和空间指标计算定量PD得分.
- 分析PD随时间和不同地理尺度的变化.
主要成果:
- 成功开发了一项用于定量PD评估的验证协议.
- 该方法允许对PD指标进行客观和可重复的测量.
- 该方法证明了能够量化街道和城市的PD变化的能力.
结论:
- 拟议的协议提供了一种强大而可扩展的方法来评估邻里身体障碍.
- 这种定量方法可以为城市规划,公共卫生战略和环境正义倡议提供信息.
- 该方法为未来研究PD在全球不同环境中的影响提供了基础.
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