深度学习基于图像的分类,用于使用无人机预测地震后的破坏水平
Norah Alsaaran1, Adel Soudani1
1Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
具有轻量级深度学习模型的无人机 (UAV) 能够快速评估地震后的损害. 这项研究表明,MobileNetV3-小边缘设备为搜索和救援团队提供了高效的实时结构损坏预测.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 遥感 遥感 遥感 遥感
背景情况:
- 无人驾驶飞行器 (UAV) 对于应对灾害至关重要.
- 基于图像的损害评估需要高效的处理.
- 轻量级的深度学习模型为内部分析提供了潜力.
研究的目的:
- 用无人机成像研究MobileNetV3-Small模型进行实时地震后损害评估.
- 评估模型在边缘设备上的性能和效率.
主要方法:
- 使用了MobileNetV3-小卷积神经网络 (CNN) 模型.
- 训练模型分类三种损伤级别 (没有,中度,严重).
- 在Raspberry Pi 5上部署和测试该模型,用于边缘计算.
主要成果:
- 移动网络V3-小实现了最低的FLOP,超过了ShuffleNetv2的58.8%.
- 微调将准确度提高了4.5%,FLOP的增加最小.
- 在合并数据集上获得0.93的加权平均F-score.
- 在Raspberry Pi 5上演示实时性能.
结论:
- 像MobileNetV3-Small这样的轻量级CNN对于机载无人机图像分析非常有效.
- 该模型允许在资源有限的环境中进行高效的实时损害评估.
- 这项技术可以显著帮助地震后的搜救 (SAR) 行动.
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