# Multi-Scale Flame Situation Detection Based on Pixel-Level Segmentation of Visual Images
## 4 Experiments
### Experimental Result and Analysis
#### 4.5.3 Case Analysis  
To demonstrate the superiority of the Flame-SeaFormer model, some images from each of the two datasets are selected for inference to visualize the quantitative segmentation results. The images from the FLAME dataset are all UAV overhead shots where the flame targets are hidden and small, making it difficult to distinguish the corresponding flame-burning stages. Therefore, small-scale flame segmentation result analysis is performed on this dataset. The images in Our_flame_smoke are derived from multiple fire videos that contain the complete burning process, and thus the analysis of the multi-scale flame segmentation results is performed on the Our_flame_smoke dataset. Further, several negative samples (i.e., images without flame targets) are chosen from the two datasets to evaluate the interference resistance capabilities of Flame-SeaFormer. In the following, small-scale flame segmentation analysis, multi-scale flame segmentation analysis, and negative sample segmentation analysis are described in detail.  
Four original images containing different numbers of fire targets are selected from the FLAME dataset for small-scale flame segmentation analysis. Employing the trained model weights for inference, the qualitative segmentation results of the four images are shown in the second row of Figure 8. Through the analysis, it can be seen that Flame-SeaFormer has superior performance in small target flame pixel segmentation. Meanwhile, Flame-SeaFormer demonstrates a certain degree of flame recognition capability, even in cases of occluded flames.  
Several fire images representing three combustion stages in the Our_flame_smoke dataset are selected for inference. The segmentation results are visually presented in Figure 11, from which several key observations can be drawn. During the initial and decaying stages, where weak flame targets exist and the number of flame pixels is relatively low, Flame-SeaFormer exhibits exceptional sensitivity and recognition accuracy in detecting subtle flame regions. In the intense flame-burning phase, characterized by visible and large-scale flames, the model accurately identifies flame regions by leveraging the global interdependence among multiple pixel points, enabling precise mapping between fine-grained pixels and flame semantics. Furthermore, Flame-SeaFormer effectively avoids misclassifying light shadows refracted by the flame as flames, demonstrating its ability to automatically filter interference features and utilize valid flame information. In summary, consistent with the quantitative results in Table 5, Flame-SeaFormer identifies and distinguishes flame regions of diverse scales throughout the entire flame-burning process. The  
Figure 10: The \(mIoU\) results of the three stages of flame combustion. (**a**) FCN vs Flame-SeaFormer. (**b**) Unet vs Flame-SeaFormer. (**c**) Deeplab v3+ vs Flame-SeaFormer. (**d**) SegFormer vs Flame-SeaFormer. (**e**) GMMSeg vs Flame-SeaFormer.  
finding validates the model's potential in quantifying flame development and supports its application in improving urban fire safety.  
Two representative negative samples from each of the two datasets are selected for inference, and the segmentation results are shown in Figure 12. For the two images from Our_flame_smoke, the color of the fire extinguisher in Figure 12a has similarity with the flame in the color hierarchy, and the refracted light shadows on the tin in Figure 12b are also confused with firelight. Nevertheless, Flame-SeaFormer demonstrates the adaptive capability to effectively filter out interfering features associated with fire-like objects. The images from the FLAME dataset feature diverse non-fire objects with varying shapes. However, Flame-SeaFormer remains resilient to the complexities of the environment. From the above analysis, it is evident that Flame-SeaFormer exhibits strong anti-interference ability.