Deep Learning-based Wildfire Smoke Detection using Uncrewed Aircraft System Imagery

Recent years have seen notable advancements in wildfire smoke detection, particularly in Uncrewed Aircraft Systems (UAS)-based detection employing diverse deep learning (DL) approaches. Despite the promise exhibited by these approaches, the task of detecting smoke from UAS imagery remains challenging due to difficulties in differentiating smoke from similar phenomena such as clouds and water. This work introduces a novel DL-based method for smoke detection from UAS visual observations. The core idea involves segregating forest areas from non-forest regions, such as sky and lake, and exclusively applying smoke detection to forested areas, thus eliminating the chance of misidentifying clouds and water as smoke. Specifically, we utilized a Mask Region-Based Convolutional Neural Network (Mask R-CNN) for semantic segmentation to remove non-forest regions (e.g., sky and lake): Subsequently, a customized You Only Look Once-version 7 (YOLOv7) model was trained to detect smoke within the forest areas. The proposed method was validated on an image dataset collected from our previous prescribed burn experiment, where we extracted 246 images to train both MASK R-CNN and YOLOv7 models. Additionally, we extract another 128 images to validate and confirm the efficacy of our enhanced wildfire smoke detection approach. The test results demonstrate that our proposed approach, employing MASK R-CNN and YOLOv7 models, outperforms the YOLOv7-only model by 25.3% in precision, 18.7% in recall, and 45% in mean Average Precision (mAP). The datasets are available at: https://github.com/khanRmahmud/wildfire-smoke-detection.