# Multi-Scale Flame Situation Detection Based on Pixel-Level Segmentation of Visual Images
## 4 Experiments
### Experimental Result and Analysis
#### 4.5.1 Visual Segmentation Results of the Global Flame-Burning Process  
The comparative results of global visual segmentation between Flame-SeaFormer and other methods on two datasets are shown in Tables 3 and 4, respectively. In particular, the results of the \(FPS\) metric for each model are visualized in Figure 9. Through careful observation, the following information is obtained. On the Our_flame_smoke dataset, GMMSeg achieves the highest segmentation accuracy, surpassing Deeplab v3+ (the lowest performer) by 1.99%, 1.18%, and 2.15% in terms of \(mAcc\), \(mF1\), and \(mIoU\), respectively. However, GMMSeg relies on a Gaussian distribution for pixel modeling, leading to the highest parameter count and the longest inference time. Flame-SeaFormer ranks third in the segmentation accuracy metrics, with the differences between GMMSeg and Flame-SeaFormer being relatively minor. Notably, Flame-SeaFormer exhibits a significant advantage in terms of \(FPS\), processing 111 frames per second. Compared with GMMSeg, Flame-SeaFormer offers a tenfold increase in inference speed with only a 10% reduction in parameter count, at the cost of a mere 0.73% decrease in \(mIoU\). When compared to SegFormer, the second highest ranked model, Flame-SeaFormer achieves a nearly three times faster inference speed while losing only 0.11% \(mIoU\). In scenarios requiring a swift response to fire incidents, the model's response speed is the most critical metric, which needs to be balanced with the inference performance. Overall, Flame-SeaFormer provides the best choice for multi-scale fire segmentation tasks, striking the best balance between accuracy and speed. Regarding the FLAME dataset, SegFormer emerges as the top performer in segmentation accuracy, attaining an \(mIoU\) of 91.06%. Flame-SeaFormer secures second place in terms of \(mAcc\), \(mF1\), and \(mIoU\), trailing SegFormer by merely 0.12% in the \(mAcc\) metric. In comparison to the worst-performing model, FCN, Flame-SeaFormer improves by 6.52%, 5.25%, and 7.78% across the three segmentation accuracy metrics. In terms of inference speed, Flame-SeaFormer still maintains its dominance, exhibiting twice the inference speed and one fifth of the model complexity when compared to SegFormer, at the expense of only a 0.12% decrease in \(mAcc\). Collectively, when dealing with small-scale flame images, Flame-SeaFormer still exhibits excellent and fast segmentation performance. In summary, Flame-SeaFormer demonstrates superior overall performance compared to the baselines across the two datasets. Meanwhile, its inference accuracy and speed align with the requirements of real-time fire information perception. This achievement is attributed to the SEA attention, which integrates crucial spatial information, facilitating \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & \(mAcc\uparrow\) & \(mF1\uparrow\) & \(mIoU\uparrow\) & \(Param\downarrow\) & \(FPS\uparrow\) \\ \hline FCN (2015) & 88.28\% & 89.06\% & 81.99\% & 35.31 M & 20.17 \\ Unet (2015) & 93.65\% & 94.11\% & 89.45\% & 43.93 M & 8.38 \\ Deeplab v3+ (2018) & 91.00\% & 92.01\% & 86.19\% & 5.81 M & 11.51 \\ SegFormer (2021) & 94.92\% & 95.10\% & 91.06\% & 44.6 M & 22.01 \\ GMMSeg (2022) & 94.16\% & 93.10\% & 87.86\% & 84.65 M & 14.57 \\  & 94.80\% & 94.31\% & 89.77\% & 8.58 M & 38.75 \\ Flame-SeaFormer (Ours) & (\(-0.12\%\)) & (\(-0.79\%\)) & (\(-1.29\%\)) & (+2.77 M) & (+16.74) \\  & **(\(-0.13\%\))** & **(\(-0.83\%\))** & **(\(-1.42\%\))** & **(+47.68\%)** & **(+76.06\%)** \\ \hline \hline \end{tabular} The explanation for this table is the same as for Table 3.  
\end{table} 
Table 4: Comparation results on FLAME (testing set). Figure 9: The \(FPS\) results on the two datasets. the enhancement of local details and effective fusion of global contextual information. Moreover, the comparative analysis of the model's performance on two datasets reveals challenges in accurately segmenting flames within the FLAME dataset.