用真实灾后街景图评估实时建筑损毁检测，提升严重程度判断准确度。
TornadoNet: Real-Time Building Damage Detection with Ordinal Supervision
- 基于序数监督设计新损失函数，让模型更懂损毁等级顺序。
- Transformer模型在严重程度排序上表现更好，序数准确率达91.15%。
- 适合灾害应急响应与遥感智能分析领域的研究人员和开发者。
我们提出TornadoNet，一个面向灾后街景图像的自动化建筑损毁评估基准，系统评估现代实时目标检测架构与序数感知监督策略在真实灾后条件下的表现。基于2021年中西部龙卷风事件的3,333张高分辨率地理标记图像与8,890个标注建筑实例，对比了YOLO系列CNN模型与RT-DETR等视觉变换器模型在五级损毁分类（基于IN-CORE标准）下的性能。实验显示：大尺寸YOLO模型在检测精度与吞吐量上最优，达到46.05% [email protected]与66-276 FPS；而RT-DETR在序数一致性上更优，实现88.13%序数Top-1准确率与MAOE=0.65。引入软序数标签与显式序数距离惩罚后，校准后的RT-DETR模型在相同设置下取得44.70% [email protected]，序数指标提升至91.15%与MAOE=0.56。结果表明，与检测架构匹配的序数监督可显著提升损毁严重程度估计效果。
原文摘要 · Abstract (English)
We present TornadoNet, a comprehensive benchmark for automated street-level building damage assessment evaluating how modern real-time object detection architectures and ordinal-aware supervision strategies perform under realistic post-disaster conditions. TornadoNet provides the first controlled benchmark demonstrating how architectural design and loss formulation jointly influence multi-level damage detection from street-view imagery, delivering methodological insights and deployable tools for disaster response. Using 3,333 high-resolution geotagged images and 8,890 annotated building instances from the 2021 Midwest tornado outbreak, we systematically compare CNN-based detectors from the YOLO family against transformer-based models (RT-DETR) for multi-level damage detection. Models are trained under standardized protocols using a five-level damage classification framework based on IN-CORE damage states, validated through expert cross-annotation. Baseline experiments reveal complementary architectural strengths. CNN-based YOLO models achieve highest detection accuracy and throughput, with larger variants reaching 46.05% [email protected] at 66-276 FPS on A100 GPUs. Transformer-based RT-DETR models exhibit stronger ordinal consistency, achieving 88.13% Ordinal Top-1 Accuracy and MAOE of 0.65, indicating more reliable severity grading despite lower baseline mAP. To align supervision with the ordered nature of damage severity, we introduce soft ordinal classification targets and evaluate explicit ordinal-distance penalties. RT-DETR trained with calibrated ordinal supervision achieves 44.70% [email protected], a 4.8 percentage-point improvement, with gains in ordinal metrics (91.15% Ordinal Top-1 Accuracy, MAOE = 0.56). These findings establish that ordinal-aware supervision improves damage severity estimation when aligned with detector architecture. Model & Data: https://github.com/crumeike/TornadoNet
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