# A Deep-Learning-Based Approach to the Classification of Fire Types
## 2 Related Work  
The literature shows that many researchers have been interested in investigating the issue of automatic fire detection using image processing. Due to the importance of early fire detection, a machine-learning-based approach has been heavily utilized to enhance the possibility of early automatic fire detection. Among the early attempts made by [10], the authors focused on the issue of reaching a fire location quickly through an automatic fire-recognition system. The proposed system utilized image processing in a MATLAB environment and RGB and YCbCr colour spaces to identify fires in a small dataset of 100 images. The results showed that the image colouring system influenced the accuracy of recognition. The authors reported accuracy scores of up to 90% with RGB and 100% with YCbCr since it reduced the influence of light on colors [10; 11]. Another work, by [11], discussed the evaluation of rules for fire detection in outdoor vegetation-fire images. The authors used 31 rules specifically for wildland-fire pixel-color detection. The rules were evaluated on a dataset of 500 fire images, categorized by colour and smoke presence for fire pixels and by luminosity for non-fire pixels. The authors used logistic regression to learn from defined rules to predict the likelihood of a pixel being a fire pixel. The features used in logistic regression were derived from the rules defined for fire detection. They employed a dataset of 500 RGB images of outdoor vegetation fires, varying sizes (from 183 \(\times\) 242 to 4000 \(\times\) 3000 pixels) and formats (jpg, ppm, bmp). These images included various lighting conditions (sunny, cloudy, night, and day). Fire areas in the images were manually segmented to create ground-truth data. Fire pixels were labelled by colour type (red, orange, white-yellow, or other) and classified as smoke or smokeless using a support vector machine. They reported the best performance using LR at 0.91. In 2016, [12] focused their work on the city of Cape Town, as it was declared one of the most fire-prone cities in South Africa. They proposed a system utilizing an ANN for wildfire risk assessment. The model used factors like climate and location as features to predict the risk rate of wildfire ignition for two different vegetation types. The system was trained on historical fire data from 2009 to 2015 and produced categorical outputs of low, moderate, high, and extreme. The authors reported an accuracy of up to 0.97 using their historical dataset. The work of [13] addressed the challenges in fire detection due to environmental factors and scene variations. It proposed a new method incorporating colour-space information into the Scale Invariant Feature Transform (SIFT) algorithm for better feature extraction. The Incremental Vector Support Vector Machine (IV-SVM) classifier was then used to build a fire-recognition model. Experiments on real-life fire images showed that this method outperformed existing ones in accuracy and speed, making it highly promising for practical applications. Later work by [14] focused on predicting and mapping fire susceptibility in Pu Mat National Park, Vietnam, using four machine learning methods: a Bayes network (BN), naive Bayes (NB), a decision tree (DT), and multivariate logistic regression (MLR). The study used data from 57 historical fires and nine explanatory variables: elevation, slope degree, aspect, average annual temperature, drought index, river density, land cover, and distance from roads and residential areas. Feature selection: The relief-F feature selection method was used to determine the importance of these variables. During validation, the BN model had the highest AUC value (0.96), followed by the DT (0.94), NB (0.939), and MLR (0.937) models. In [15], the authors presented a review of machine learning (ML) applications in wildfire science and management. The study spans the history of ML use in this field since the 1990s.