# Multi-Scale Flame Situation Detection Based on Pixel-Level Segmentation of Visual Images
## 1 Introduction
For the two fire videos, 45 frames of images (including three processes of combustion) were selected from each for flame parameter analysis. These image samples were subjected to the calculation process described in Figure 3b to obtain the flame height, width, and area in each frame of image, as well as the image contrast. The obtained flame parameter trends are shown in the middle of Figure 2. Initially, the size of the fire region is small. As the flames spread, the values of the flame parameters rapidly increase until they reach a stable state. After complete combustion, the flames gradually diminish, resulting in a decrease in each parameter value. Figure 2a shows the flame temperature data detected by [PERSON] et al. using sensors at different locations in their experimental scenario [1]. They observed that the fire process also exhibited three-stage characteristics. The five sub-figures show that the vision-based fire detection method captures flame motion patterns similar to those obtained through sensor-based methods. The visual fire detection method can be extended to realistic fire scenes, such as the home fire scene in the lower part of Figure 2. With the rapid development of computer vision technology and intelligent monitoring systems, vision-based methods offer faster response times and lower misjudment rates than traditional sensors. The strength significantly contributes to accelerating the intelligent development of fire safety in urban areas. In recent years, scholars have extensively explored fire detection methods from the perspectives of image classification, object detection, and semantic segmentation. The method based on image classification aims to determine whether smoke or flame is present in an image. [PERSON] et al. optimized a convolutional neural network (CNN) model and combined it with the RGB color space model to extract relevant fire features [3]. [PERSON] et al. proposed a real-time fire detection framework called E-FireNet, specifically designed for complex monitoring environments [4]. The object detection-based fire detection method aims to annotate fire objects in images using rectangular bounding boxes. [PERSON] et al. employed an enhanced YOLOv4 network capable of detecting fire areas by adapting to diverse weather conditions [5]. [PERSON] et al. accelerated the detection speed by extracting key frames from a video and subsequently locating fires using superpixels [6]. The fire detection method based on semantic segmentation detects the fire objects' contours by determining whether each pixel in the fire image is a fire pixel. This approach has potential to gain fine-grained parameter information (such as flame height, width, and area) in complex fire scenarios. [PERSON] et al. proposed a rule-based color model, which employed the RGB and YCbCr color spaces to allow the simultaneous detection of multiple fires [7]. Figure 3: Schematic diagram of experimental setup and flame parameters. [PERSON] et al. introduced an attention-guided optical satellite video smoke segmentation network that effectively suppresses the ground background and extracts the multi-scale features of smoke [8]. [PERSON] et al. utilized two vision-based variants of Transformer (TransUNet and MedT) to explore the potential of visible spectral images in forest fire segmentation [9]. Many fire segmentation methods have achieved notable results. However, flames exhibit variable shapes and sizes, and their edges are blurred, posing challenges to accurate segmentation. Therefore, little attention is paid to further mining fire situation information through fire segmentation. To solve the issue, we propose a flame situation detection method called Flame-SeaFormer, which utilizes the pixel-level segmentation results of images to obtain flame parameters. By analyzing these flame parameters, valuable insights into fire propagation behavior are derived, offering crucial references for firefighting decision making and enabling the development of intelligent fire safety systems.