# Multi-Scale Flame Situation Detection Based on Pixel-Level Segmentation of Visual Images
## 4 Experiments
### Experimental Result and Analysis
#### 4.5.2 Visual Segmentation Results of the Local Flame-Burning Stage  
As described in the Introduction, this paper divides the flame combustion process into three stages: the initial fire, the violent burning, and the decaying extinguishing stages. The images on the FLAME dataset were captured from a top-down perspective using a drone, and it is difficult to distinguish the corresponding flame-burning stage of each image. Consequently, a local analysis of FLAME is omitted from this study. Instead, the focus is on evaluating the performance of Flame-SeaFormer and other methods on the Our_flame_smoke dataset, specifically concerning the visual segmentation results at each stage. The segmentation accuracy metric \(mIoU\) is selected as the evaluation criterion. The results are presented in Table 5, with the stages conveniently labeled as early, midterm, and later. Furthermore, the \(mIoU\) metric for each stage is visualized in a histogram shown in Figure 10. Through observation, the following information is obtained.  
Flame-SeaFormer exhibits superior segmentation performance across the three distinct stages of flame combustion. Compared to Unet, which is considered the top-performing model in the CNN architecture category, Flame-SeaFormer achieves significant improvements of 0.91%, 0.49%, and 0.96% in \(mIoU\) for the respective stages. In comparison to GMMSeg, the top-performing Transformer-based model, Flame-SeaFormer showcases comparable performance during the intense flame-burning phase, while outperforming GMMSeg by 0.83% and 0.59% in \(mIoU\) for the fire initiation and decay extinction phases, respectively. The analysis of the flame-burning stages reveals distinct characteristics. During the initial stage, there is an observable increase in the scale of the flames, while, in the intense combustion phase, the flames spread slowly and remain stable, reaching their maximum scale. In the flame extinction stage, the flames gradually diminish, resulting in a reduction in scale. There exist flame objects of varying shapes and scales during the combustion process. Flame-SeaFormer has excellent segmentation quality for multi-scale flame targets. Apparently, the SEA attention and fusion block enable the model to adaptively capture low-level spatial and high-level semantic features in flames of different scales. This capability allows the model to perceive flame edge details and enhance the accuracy of flame parameter estimation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{\(mIoU\)} \\ \cline{2-4}  & **Early** & **Midterm** & **Later** \\ \hline FCN (2015) & 89.81\% & 95.04\% & 89.32\% \\ Unet (2015) & 91.60\% & 95.59\% & 90.68\% \\ Deeplab v3+ (2018) & 89.81\% & 94.91\% & 88.69\% \\ SegFormer (2021) & 90.89\% & 95.61\% & 90.55\% \\ GMMSeg (2022) & 91.68\% & 96.08\% & 91.05\% \\ Flame-SeaFormer (Ours) & **92.51\%** & **96.08\%** & **91.64\%** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Segmentation results of the three stages of flame combustion on Our_flame_smoke.