Automated Image-Based Identification and Consistent Classification of Fire Patterns with Quantitative Shape Analysis and Spatial Location Identification Article Swipe

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Pengkun Liu , Shaoxiang Ni , Stanislav I. Stoliarov , Pingbo Tang ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2410.23105

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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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2410.23105
PDF: https://arxiv.org/pdf/2410.23105
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Cited By: 1
Related Works: 10
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OpenAlex ID: https://openalex.org/W4404343106

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DOI: https://doi.org/10.48550/arxiv.2410.23105

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Title: Automated Image-Based Identification and Consistent Classification of Fire Patterns with Quantitative Shape Analysis and Spatial Location Identification

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-10-30

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Authors: Pengkun Liu, Shaoxiang Ni, Stanislav I. Stoliarov, Pingbo Tang

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Landing page: https://arxiv.org/abs/2410.23105

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PDF URL: https://arxiv.org/pdf/2410.23105

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Concepts: Identification (biology), Artificial intelligence, Pattern recognition (psychology), Computer science, Computer vision, Cartography, Geography, Botany, Biology

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Cited by: 1

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Citations by year (recent): 2024: 1

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Related works (count): 10

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