Using Neural Networks to Detect Fire from Overhead Images

Abstract

The use of artificial intelligence (AI) is increasing in our everyday applications. One emerging field within AI is image recognition. Research that has been devoted to predicting fires involves predicting its behaviour. That is, how the fire will spread based on environmental key factors such as moisture, weather condition, and human presence. The result of correctly predicting fire spread can help firefighters to minimise the damage, deciding on possible actions, as well as allocating personnel effectively in potentially fire prone areas to extinguish fires quickly. Using neural networks (NN) for active fire detection has proven to be exceptional in classifying smoke and being able to separate it from similar patterns such as clouds, ground, dust, and ocean. Recent advances in fire detection using NN has proved that aerial imagery including drones as well as satellites has provided great results in detecting and classifying fires. These systems are computationally heavy and require a tremendous amount of data. A NN model is inextricably linked to the dataset on which it is trained. The cornerstone of this study is based on the data dependencieds of these models. The model herein is trained on two separate datasets and tested on three dataset in total in order to investigate the data dependency. When validating the model on their own datasets the model reached an accuracy of 92% respectively 99%. In comparison to previous work where an accuracy of 94% was reached. During evaluation of separate datasets, the model performed around the 60% range in 5 out of 6 cases, with the outlier of 29% in one of the cases.

An Attention-Guided Deep-Learning-Based Network with Bayesian Optimization for Forest Fire Classification and Localization

“…To mitigate overfitting, they utilized a single fully connected layer in the classifier. In their study [31], L. Kurasinski et al explored how dataset variation influences model performance, training on two distinct datasets (FLAME and NASA) and conducting cross-validation on FLAME, NASA, and a GitHub dataset. The primary objective is to gain insights into how the choice of dataset impacts the performance of the Xception model, as referenced in [29].…”