# Multi-Scale Flame Situation Detection Based on Pixel-Level Segmentation of Visual Images
## 2 Related Work  
Fire segmentation detects fire pixels in fire images by separating the foreground of smoke or flame from the background. It avoids using bounding boxes and focuses on delineating distinct boundaries. There are two categories of fire segmentation methods, feature analysis-based methods and deep learning-based methods, as shown in Figure 4.  
**Feature analysis-based methods:** Fire objects exhibit rich visual characteristics, encompassing both static features like color, texture, and shape, and dynamic features such  
Figure 4: Overview of relevant research on fire segmentation. as diffusion, displacement, and flicker [10]. Researchers have employed distinct methods to extract these features and construct comprehensive feature representations to enable effective recognition. [PERSON] et al. employed the YCbCr color space instead of RGB to construct a universal chromaticity model for flame pixel classification [11]. [PERSON] et al. fused two-dimensional Otst and HSI color gamut growth to handle reflections and smoke areas, efficiently segmenting suspected fire regions [12]. [PERSON] et al. combined optical flow, scatter, and intensity features in infrared fire images for discriminative segmentation features [13]. [PERSON] et al. presented a fire recognition method based on enhanced color segmentation and multi-feature description [14]. The approach employs the area coefficient of variation, centroid dispersion, and circularity of images as statistics to determine the optimal threshold in fine fire segmentation. [PERSON] et al. compared the Sobel and Canny edge detection techniques, finding that Canny edge detection suppresses noisy edges better, achieving higher accuracy and facilitating fire growth detection [15]. **Deep learning-based methods:** Deep learning methods have gained prominence in computer vision across various application domains [16]. These methods utilize deep neural networks to automatically learn feature representations through end-to-end supervised training. Researchers have made substantial progress by improving classical CNN for fire segmentation. [PERSON] et al. proposed a CNN-based smoke segmentation method that fuses information from two paths to generate a smoke mask [17]. [PERSON] et al. employed Mask RCNN to identify and segment indoor combustible objects and predict the fire load [18]. [PERSON] et al. applied the Deeplab v3+ framework to segment flame areas in limited aerial images [19]. [PERSON] et al. introduced a multi-resolution iterative tree search algorithm for flame and smoke region segmentation [20]. [PERSON] et al. selected four classical semantic segmentation models (Unet, Deeplab v3+, FCN, and PSPNet) with two backbones (VGG16 and ResNet50) to analyze forest fires [21]. They found the Unet model with the ResNet50 backbone to have the highest accuracy. [PERSON] et al. introduced a novel CNN-based network for smoke and flame detection in forests [22]. The network generates accurate flame and smoke masks in RGB images, reducing false alarms caused by clouds or haze. Attention mechanisms have also been employed, such as in ATT Squeeze Unet and Smoke-Unet, to focus on salient fire features [23; 24]. [PERSON] et al. introduced Smoke-Unet, an improved Unet model that combines an attention mechanism and residual blocks for smoke segmentation [24]. It utilizes high-sensitivity bands and remote sensing indices in multiple bands to detect forest fire smoke early. CNN-based models are effective for fire detection and segmentation, but they struggle in capturing global image information. Transformer-based models, with their self-attention mechanism, excel at capturing global features. [PERSON] et al. employed two Transformer-based models (TransUnet and TransFire) and a CNN-based model (EfficientSeg) to identify precise fire regions in wildfires [25].