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Real-time quantitative analysis of wildfire fireline merging behavior based on segmentation-skeletonization algorithm
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AI
DOI：10.1016/j.engappai.2025.113310.png)
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Abstract
En 中文
Multiple interacting wildfires often produce fireline merging, which can abruptly intensify fire potential and trigger high-risk phenomena such as deflagration and fire jumping. Real-time, quantitative identification of fireline merging behavior is therefore critical for scientific decision-making and efficient emergency response. In this paper, we propose an artificial intelligence framework that integrates deep-learning-based image segmentation with skeletonization analysis to quantify fireline merging behavior and mitigate the problems of lagged capture and low recognition accuracy in current fire monitoring. The framework adopts the You Only Look Once version 8 segmentation (YOLOv8-seg) model integrated with Bottleneck Transformer Block (BoTBlock), which combines convolutional feature extraction with multi-head self-attention (MHSA) to enhance global semantic perception and improve fireline segmentation accuracy and boundary consistency. Building on this, topological skeleton extraction and Basis spline (B-spline) curve smoothing are used to obtain the main fireline path, while curvature extremum points are employed to automatically locate the merging position and characterize the merging trend. Experiments show that the system achieves an inference speed of 30 frames/second and a segmentation mean Average Precision at Intersection over Union(IoU) 0.5–0.95 (mAP50–95) of 83.3 %, effectively balancing real-time performance and accuracy. Comparisons with typical field measurements and published studies indicate that the proposed method can reliably recover the trajectory and key behavioral characteristics of fireline merging in dynamic fire scenes.
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