2024-05-28 カナダ・コンコーディア大学
Photo by Landon Parenteau on Unsplash
＜関連情報＞
- https://www.concordia.ca/news/stories/2024/05/28/the-future-of-fighting-wildfires-will-rely-on-autonomous-drone-based-artificial-intelligence-according-to-concordia-researchers.html
- https://ieeexplore.ieee.org/document/10520273
空中可視赤外線画像による山火事の早期発見と距離推定 Early Wildfire Detection and Distance Estimation Using Aerial Visible-Infrared Images
Linhan Qiao; Shun Li; Youmin Zhang; Jun Yan
IEEE Transactions on Industrial Electronics Published:03 May 2024
DOI:https://doi.org/10.1109/TIE.2024.3387089
Abstract
This article proposes a novel deep-learning-based ORB-SLAM-feature filtering framework to monitor, detect the occurrence, and estimate the distance of early wildfire through an integrated design of image processing of aerial onboard visual-infrared sensor measurements and real-time navigation of an unmanned aerial vehicle (UAV). The proposed framework uses a DJI ZenMuse H20T onboard sensor integrating with both visual and infrared cameras mounted on a DJI M300 UAV. It consists of three main functional modules to support early wildfire fighting and management missions: 1) smoke and suspected flame segmentation based on an attention gate U-Net, which decreases false alarm and provides semantic information; 2) camera poses recovery based on a monocular SLAM algorithm and wildfire spot distance estimation based on a triangulation algorithm. With the estimated wildfire distance, camera poses, and global positioning system (GPS) information of the UAV, the suspected wildfire spot can be geo-located; 3) visual-infrared images registration based on a geometry model to forbid false detection and missing segmentation. Finally, independent indoor and outdoor experiments are conducted to verify the effectiveness of the proposed algorithms in the developed framework.
