Unsupervised Method for Wildfire Flame Segmentation and Detection

Abstract

In the last decade, there have been many reports on the negative impact of wildfires on various ecosystems. Unfortunately, wildfires have been intensifying as global temperatures, droughts, and other instances of extreme weather events rise around the world. These circumstances are forcing communities to vigorously address the uncontrolled spread of wildfires, where the ultimate goal is the protection of wildlife. At the same time, many disaster prevention and monitoring methods, based on image processing and computer vision, have been developed. In this paper, we present a new unsupervised method based on RGB color space for the early detection of wildfires from still images. From the analysis of existing state-ofthe-art methods, it is evident that different methods explore different color spaces for the extraction of flame features. Our motivation was to use only RGB color space and thus eliminate the time-consuming task of color space conversion. The proposed method consists of several new image processing techniques used to efficiently extract flame features. It outperforms the existing methods, where an increase of 3% and 2% is recorded in the F1 score and Matthews correlation coefficient, respectively. Such performance demonstrates the merits of the proposed method for flame segmentation and detection.

Dynamic attention network for flame detection

“…[20] introduce a knowledge distillation framework. An unsupervised method is proposed for uniformly wildfire flame segmentation and detection [21]. Li et al.…”

“…Sun et al [18] build a flame detection system based on YOLOv4 [19], while Zhou et al [20] introduce a knowledge distillation framework. An unsupervised method is proposed for uniformly wildfire flame segmentation and detection [21]. Li et al [22] present a lightweight algorithm based on YOLOv3 [23] and achieve fast flame detection.…”