用量化分析提升火灾痕迹分类准确率，辅助调查更客观。
Automated Image-Based Identification and Consistent Classification of Fire Patterns with Quantitative Shape Analysis and Spatial Location Identification
- 结合人工与算法提取火场痕迹，实现人机协同分析
- 合成数据上分类精度达93%，真实场景达83%
- 适合消防调查员、刑侦技术人员使用
火灾痕迹包含反映火势行为与起火点的重要信息，传统分类依赖调查人员的视觉判断，易产生主观偏差。本文提出一种定量火灾痕迹分类框架，旨在提升分类的一致性与准确性。该框架包含四个部分：首先通过人机协作从表面提取火灾痕迹，融合调查员经验与计算分析；其次采用基于长宽比的随机森林模型对痕迹形状进行分类；第三，利用火场点云分割精准识别受火影响区域，并将二维痕迹映射至三维场景；最后，通过痕迹与室内物体的空间关系支持火场还原分析。该框架融合定性与定量数据，实现系统化火灾痕迹分析。在合成数据上分类精度达93%，真实火灾痕迹分类准确率为83%。
原文摘要 · Abstract (English)
Fire patterns, consisting of fire effects that offer insights into fire behavior and origin, are traditionally classified based on investigators' visual observations, leading to subjective interpretations. This study proposes a framework for quantitative fire pattern classification to support fire investigators, aiming for consistency and accuracy. The framework integrates four components. First, it leverages human-computer interaction to extract fire patterns from surfaces, combining investigator expertise with computational analysis. Second, it employs an aspect ratio-based random forest model to classify fire pattern shapes. Third, fire scene point cloud segmentation enables precise identification of fire-affected areas and the mapping of 2D fire patterns to 3D scenes. Lastly, spatial relationships between fire patterns and indoor elements support an interpretation of the fire scene. These components provide a method for fire pattern analysis that synthesizes qualitative and quantitative data. The framework's classification results achieve 93% precision on synthetic data and 83% on real fire patterns.
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