用深度学习自动评估灾后建筑损伤，提升救援效率
Accelerating Post-Tornado Disaster Assessment Using Advanced Deep Learning Models
- 结合YOLOv11与ResNet50，快速分析灾后影像中的建筑损伤
- ResNet50在多类损伤分类中达90.28%准确率，单图推理仅需1529毫秒
- 适合应急响应与灾后重建团队快速获取客观评估结果
灾后建筑与基础设施的评估对紧急救援和长期韧性规划至关重要。本研究提出一种基于先进深度学习模型的自动化评估方法，利用前沿计算机视觉技术（YOLOv11和ResNet50）快速分析灾难现场的图像与视频，提取结构构件损伤等级及破坏范围等关键信息。实验结果显示，ResNet50在多类损伤分类任务中达到90.28%的准确率，单张图像推理时间仅为1529毫秒。该研究为灾害管理领域提供了可扩展、高效且客观的灾后分析工具，有望改变社区与机构应对和从灾难中学习的方式。
原文摘要 · Abstract (English)
Post-disaster assessments of buildings and infrastructure are crucial for both immediate recovery efforts and long-term resilience planning. This research introduces an innovative approach to automating post-disaster assessments through advanced deep learning models. Our proposed system employs state-of-the-art computer vision techniques (YOLOv11 and ResNet50) to rapidly analyze images and videos from disaster sites, extracting critical information about building characteristics, including damage level of structural components and the extent of damage. Our experimental results show promising performance, with ResNet50 achieving 90.28% accuracy and an inference time of 1529ms per image on multiclass damage classification. This study contributes to the field of disaster management by offering a scalable, efficient, and objective tool for post-disaster analysis, potentially capable of transforming how communities and authorities respond to and learn from catastrophic events.
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