# A Deep-Learning-Based Approach to the Classification of Fire Types
## 3 Experimental Framework
### Data Collection  
Our investigation involved examining the currently publicly available datasets. A considerable number of existing datasets cover this topic. However, they all focus on binary classifying instances of fire vs. no fire. It became apparent that there is a lack of publicly accessible datasets that incorporate classification according to fire classes. Consequently, this work embarked on the project of creating a dataset that will be made publicly available to foster additional inquiries by scholars within the field and allow the reproduction of the results. To create a new dataset covering instances spanning the major fire classes, we opted to utilize some currently available binary (fire vs. no fire) datasets that mostly focus on natural wildfire [23; 24], which essentially meant training models to automatically detect \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Work** & **Dataset** & **Approach** &
\begin{tabular}{c} **Type of** \\ **Classification** \\ \end{tabular} & **Results** \\ \hline
[10] & 100 images & \
\begin{tabular}{c} MATLAB and \\ RGB \& YCbCr \\ \end{tabular} &
\begin{tabular}{c} Binary (fire vs \\ no fire) \\ \end{tabular} & 90–100\% acc. \\ \hline
[11] & \begin{tabular}{c} 500 wildfire \\ images \\ \end{tabular} & \
\begin{tabular}{c} Rule-based, LR, \\ and SVM \\ \end{tabular} &
\begin{tabular}{c} Binary (fire vs. \\ no fire) \\ \end{tabular} & 0.91 F \\ \hline
[12] & \
\begin{tabular}{c} Historical data \\ for Cape Town \\ \end{tabular} & ANN &
\begin{tabular}{c} Low, moderate, \\ high, \\ and extreme \\ \end{tabular} & 0.97 Acc. \\ \hline
[18] & \
\begin{tabular}{c} Real-time \\ sensor-based \\ \end{tabular} &
\begin{tabular}{c} Three types of \\ sensors \\ \end{tabular} & Fire existence & not mentioned \\ \hline
[14] & 57 recorded fires & \
\begin{tabular}{c} NB, DT, and \\ MLR \\ \end{tabular} &
\begin{tabular}{c} Binary (fire vs. \\ no fire) \\ \end{tabular} & AUC value: 0.96 \\ \hline
[17] & \
\begin{tabular}{c} Real-time \\ sensor-based \\ \end{tabular} & Fuzzy logic &
\begin{tabular}{c} Detecting fire \\ symptoms \\ \end{tabular} & not mentioned \\ \hline
[19] & \begin{tabular}{c} Historical fires, \\ sensors, and \\ simulation \\ \end{tabular} & \
\begin{tabular}{c} SVM, DT, \\ ANFIS, and \\ ANN \\ \end{tabular} &
\begin{tabular}{c} Binary (fire vs. \\ no fire) \\ \end{tabular} & 98\% accuracy \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of previous work on image-based fire detection and classification. merely the existence of fire. In addition, new images are added and manually annotated into the main classes of fire. Table 2 shows the characteristics of our Fire Classification Dataset (FCD), intended to be released as a part of this work to allow further research explorations [25]. To annotate instances in our dataset, the guideline for different fire characteristics provided by the Fire Safe organization in the United Kingdom was used [9]. The Solid-material fire is the default class of fire images. In contrast, the electrical fire is detected at the beginning of a fire since it usually starts with an enlightened spark. Moreover, the flame of a chemical fire can be of different colours, such as green, blue, red, or violet. An oil fire can be recognized through its smoke colour and fire location [9]. To ensure the quality of the images used and the correctness of the annotation, the collected images were from different resources like video frames of actual, real-life events. For instance, Figure 1 shows a sample image of a real-life oil refinery fire incident in Iraq and was reported on the BBC's news outlet [26]. The images in the dataset were carefully selected to cover a wide range of different possible scenarios, e.g., daylight and night effects. The images also capture various stages of fire, which can help identify as many features as possible for each fire type. Figure 2 shows an example image of a chemical fire with the colour of the flame as the most distinguishing feature [27].