forest_fire_detection_system
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online early forest fire detection system based on drone platform.
Forest Fire Detection System
This ROS package is designed for early wildfire detection, geolocation and monitoring.
System architecture
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Functions
Early Wildfire Flame and Smoke Segmentation
functions | results |
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path planning | ![]() |
forest fire image classification | ![]() |
forest fire image segmentation | ![]() |
gimbal control | ![]() |
fire point geolocation | ![]() |
Multi-view Geometry-based Wildfire Spot Geolocation
Early wildfire spot perception methods
functions | |
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Attention gate U-net wildfire segmentation | ![]() |
Trianglulation-based wildfire point depth estimation | ![]() |
Visible-infrared camera system calibration | ![]() |
Model-based wide fire point registration | ![]() |
Wildfire Local Environment 3D reconstruction
You can also compare the reconstruction results with google earth.
SFM with colmap |
Reconstruction with OpenMVS |
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Outdoor Flight Test Videos
M300 drone flies along the defined zigzag path, while the attention gate U-net is implemented to detect and segment the suspect wildfire.
- Once the suspect fire is detected, M300 will fly from left to the right. In the meantime, a monocular SLAM is running to acquire the precise camera pose, the GPS information is used to correct the scale of the poses.
- Based-on the triangulation of the fire zone, the distance can be estimated. Then, the fire can be geolocated by with the gimbal angle.
- Wildfire Local Environment 3D reconstruction
Finally, M300 will fly along a circle shape of the flight path to record the scene of the local environment around the wildfire spot with the H20T zoom camera.
Related Paper
@INPROCEEDINGS{9836119,
author={Li, Shun and Qiao, Linhan and Zhang, Youmin and Yan, Jun},
booktitle={2022 International Conference on Unmanned Aircraft Systems (ICUAS)},
title={An Early Forest Fire Detection System Based on DJI M300 Drone and H20T Camera},
year={2022},
volume={},
number={},
pages={932-937},
doi={10.1109/ICUAS54217.2022.9836119}}
Copyright
Copyright (C) 2021 Concordia NAVlab. All rights reserved.