U3UNet: An accurate and reliable segmentation model for forest fire monitoring based on UAV vision

Abstract

Forest fires pose a serious threat to the global ecological environment, and the critical steps in reducing the impact of fires are fire warning and real-time monitoring. Traditional monitoring methods, like ground observation and satellite sensing, were limited by monitoring coverage or low spatio-temporal resolution, making it difficult to meet the needs for precise shape of fire sources. Therefore, we propose an accurate and reliable forest fire monitoring segmentation model U3UNet based on UAV vision, which uses a nested U-shaped structure for feature fusion at different scales to retain important feature information. The idea of a full-scale connection is utilized to balance the global information of detailed features to ensure the full fusion of features. We conducted a series of comparative experiments with U-Net, UNet 3＋, U2-Net, Yolov9, FPS-U2Net, PSPNet, DeeplabV3＋ and TransFuse on the Unreal Engine platform and several real forest fire scenes. According to the designed composite metric S, in static scenarios 71. 44% is achieved, which is 0.3% lower than the best method. In the dynamic scenario, it reaches 80.53%, which is 8.94% higher than the optimal method. In addition, we also tested the real-time performance of U3UNet on edge computing device equipped on UAV.