AutoDL-based convolutional neural networks for wildfire detection

Abstract

Wildfires have emerged as a pressing issue in many regions of the world due to the ongoing impact of global warming on the planet. However, a reliable and high-performance detection system is currently lacking. This study strives to introduce a wildfire detection system that is based on neural networks and image recognition. This study utilized Edge Impulse to train a neural network to identify wildfire occurrences in the given pictures. To optimize the performance and adaptability of the model, an extensive dataset was compiled by collating images from two Kaggle projects, resulting in a final dataset of over 3000 images. The core technological advancements that Edge Impulse is based on are Auto Deep Learning (AutoDL) and Convolutional Neural Networks (CNN). By applying technologies like Neural Architecture Search (NAS), hyperparameters optimization, and transfer learning, AutoDL enables people interested in machine learning to approach the technology without an extensive understanding of math or programming that machine learning was built upon. CNN is a highly effective and efficient form of neural network popular for image classification. It consists of three different layers: the convolutional layer, the pooling layer, and the fully connected layer. The result of this study consists of a fully functional model for wildfire detection that is ready to be deployed, with a final testing accuracy of over 99%.