Early Wildfire Detection Using Convolutional Neural Network

EasyChair Preprint no. 2581

Abstract

Wildfires are one of the disasters that are difficult to detect early and cause significant damage to human life, ecological systems, and infrastructure. There have been several research attempts to detect wildfires based on convolutional neural networks (CNNs) in video surveillance systems. However, most of these methods only focus on flame detection, thus they are still not sufficient to prevent loss of life and reduce economic and material damage. To tackle this issue, we present a deep learning-based method for detecting wildfires at an early stage by identifying flames and smokes at once. To realize the proposed idea, a large dataset for wildfire is acquired from the web. A light-weight yet powerful architecture is adopted to balance efficiency and accuracy. And focal loss is utilized to deal with the imbalance issue between classes. Experimental results demonstrate the effectiveness of the proposed method and validate its suitability for early wildfire detection in a video surveillance system.

Keyphrases: deep learning, Early wildfire detection, video surveillance

@Booklet{EasyChair:2581,
  author = {Seon Ho Oh and Sang Won Ghyme and Soon Ki Jung and Geon-Woo Kim},
  title = {Early Wildfire Detection Using Convolutional Neural Network},
  howpublished = {EasyChair Preprint no. 2581},

  year = {EasyChair, 2020}}