BurNet: automated deep learning system for fire and smoke detection from drone imagery

Automatic identification of wildfires is an area attracting great interest in the past decade. Early detection of fire can help in minimizing disasters and assist decision makers to plan mitigation methods. In this paper, we annotate and utilize a drone imagery dataset with each of its pixels marked as: (a) Burning, (b) Burned, and (c) Unburnt. The dataset is comprised of 22 videos (138,390 frames) among which only a subset of 481 frames (~20 frames from each video) are marked for segmentation. In addition, the entire suite of frames is categorized as either “Smoke” or “No-Smoke”. We implement DeepLab-v3+ architecture to accurately segment affected regions as “Burned”, “Burning”, and “Unburnt”. We adopt a transfer learning-based architecture using an established Xception network to detect smoke within each frame to identify regions that can affect the performance of the proposed segmentation approach. Our segmentation algorithm achieves a mean accuracy of 97% and mean Jaccard Index of 0.93 on three test videos comprising 24,666 frames across all categories. Our classification algorithm achieves 92% for identifying smoke in each of those test frames.