Abstract
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.
Abstract (translated)
火灾模式，由提供有关火灾行为和起火点见解的火灾效果组成，传统上是根据调查人员的视觉观察进行分类的，这导致了主观解释。本研究提出了一种量化火灾模式分类框架，以支持火灾调查员，旨在实现一致性和准确性。该框架整合了四个组成部分。首先，它利用人机交互从表面提取火灾模式，结合了调查员的专业知识与计算分析。其次，采用基于纵横比的随机森林模型对火灾模式形状进行分类。第三，火灾现场点云分割能够精确识别受火影响区域，并将二维火灾模式映射到三维场景中。最后，火灾模式和室内元素之间的空间关系有助于解释火灾现场情况。这些组成部分提供了一种综合定性和定量数据的火灾模式分析方法。该框架在合成数据上的分类结果达到93%的精度，在真实火灾模式上达到83%。
URL
https://arxiv.org/abs/2410.23105
