Early Wildfire Detection and Distance Estimation Using Aerial Visible-Infrared Images
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".