# Fire Video Recognition Based on Local and Global Adaptive Enhancement
## 2 Related Work
### Datasets for Fire Warnings  
Being the foundation of fire warnings, the pursuit of better datasets never stops. As shown in Table 1, there are some public datasets used for fire warnings, significantly advancing research in this field.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Year** & **Type** & **Number** & **Main Metric** \\ \hline Chino’s dataset [9] & 2015 & Still image & 226 & F1 score \\ Foggia’s dataset [10] & 2015 & Video & 31 & Accuracy \\ Corsican fire dataset [11] & 2017 & Still image/video & \(600/5\) & F1 score \\ FireNet dataset [12] & 2019 & Still image/video & \(2585/62\) & Accuracy \\ DFS dataset [13] & 2023 & Still image & 9462 & mAP50 and mAP75 \\ LFVR dataset & 2024 & Video & 11,560 & F1 score \\ \hline \hline \end{tabular}
\end{table}
Table 1: Datasets about fires.  
A fire recognition dataset was proposed in [9], which includes 226 still images with different resolutions. These images are categorized into two groups, i.e., 107 images without fires and 119 images containing fires. In the images containing fires, there are buildings on fires, industrial fires, car accidents, and riots. In images without fires, there are fire-like regions, e.g., sunsets, red or yellow objects, etc. Regarding evaluation, the F1 score was selected as the main metric.  
In order to provide a video dataset for fire recognition, a fire video dataset was constructed [10]. In this dataset, there are 31 videos, of which 14 videos include fires and 17 videos have no fires. These videos were acquired both from real environments and the Internet. Among them, 80% of samples were utilized for testing, and 20% were employed for training. Moreover, accuracy was chosen as the primary metric.  
The Corsican fire dataset is about wildfires in images and videos [11]. In this dataset, there are 500 still images of wildfires, 100 near-infrared images, and 5 multi-modal fire videos. All images in this dataset are correlated with corresponding annotations and descriptors. Moreover, the F1 score is used as the primary metric.  
To create a diverse fire dataset, FireNet [12] was built by shooting videos and collecting images from the Internet. The training set of FireNet includes 1124 images including flames and 1301 images excluding flames. The test set of FireNet consists of 46 videos containing flames, 16 videos excluding flames, and another 160 images without flames. Furthermore, accuracy is the main metric.  
To promote studies in fire detection, DFS was created by collecting images from real scenes [13], and it consists of 9462 images. Among them, there are 3357 images including large fires, 4722 images containing intermediate fires, 349 images including small fires, and 1034 images containing luminous objects. Three categories (i.e., \"fire\", \"smoke\", and \"other\") are defined for fire detection. Moreover, mAP50 and mAP75 are used as metrics for evaluation.  
The aforementioned classic public datasets promote research progress in the field of fire recognition. In general, the motion information of flames is the key factor for accurately recognizing fires. However, images cannot describe the temporal information of flame motion. Therefore, large-scale video datasets for fire recognition are significant for fire warnings.