The Application of CNN-Based Image Classification To Wildfire Early Detection

Abstract

Wildfires have become increasingly common and devastating in recent years, yet many regions still rely on traditional methods of using human spotters atop lookout towers to detect fires. These methods are slow and inefficient, and as fires become more frequent demand for new detection methods has grown along with it. This study aims to utilize artificial intelligence along with stationary camera systems to perform early detection of wildfires. An open-source dataset of 1900 wildfire pictures was obtained and processed, and a convolutional neural network model was trained on the dataset. The final model achieved an accuracy of 95.59% on the validation dataset after being trained for 37 epochs.