LW-FIRE: A Lightweight Wildfire Image Classification with a Deep Convolutional Neural Network

Applied Sciences

Emir Buza¹  Amila Akagic¹ 

[1] Faculty of Electrical Engineering, University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina;

关键词: artificial neural networks;    computer vision;    machine learning;    deep learning;    deep neural networks;    image classification;   

DOI  :  10.3390/app12052646

来源: DOAJ

【 摘 要 】

Analysis of reports published by the leading national centers for monitoring wildfires and other emergencies revealed that the devastation caused by wildfires has increased by 2.96-fold when compared to a decade earlier. The reports show that the total number of wildfires is declining; however, their impact on the wildlife appears to be more devastating. In recent years, deep neural network models have demonstrated state-of-the-art accuracy on many computer vision tasks. In this paper, we describe the design and implementation of a lightweight wildfire image classification model (LW-FIRE) based on convolutional neural networks. We explore different ways of using the existing dataset to efficiently train a deep convolutional neural network. We also propose a new method for dataset transformation to increase the number of samples in the dataset and improve the accuracy and generalization of the deep learning model. Experimental results show that the proposed model outperforms the state-of-the-art methods, and is suitable for real-time classification of wildfire images.

【 授权许可】

Unknown