Early Forest Fire Detection and Localization Using AAV Bimodal Images
Bibliographic record
Abstract
In this paper, a novel early forest fire detection and localization system is proposed based on an unmanned aerial vehicle (UAV) equipped with RGB and thermal cameras. The forest fire detection is achieved by the proposed bimodal detection network. Fire localization, i.e., fire distance estimation, is realized by the proposed monocular depth estimation network and scale recovery strategy. To validate the effectiveness of the proposed method, the performance of the detection network is tested using the bimodal dataset UAVFire presented in this paper and the public dataset FLAME2. The proposed depth estimation network is validated on the public dataset WildUAV. The experimental results show that the proposed bimodal detection network has a AP of 97.9% and 98.2% on the UAVFire and FLAME2 datasets, respectively, with a model parameter of 6.5 MB. Compared with other unimodal detection methods as well as bimodal detection methods, the detection method in this paper performs well in terms of both the detection accuracy and the model lightweighting. The model parameter of the proposed monocular depth estimation network is only 0.69 MB, and the comparison results with other methods on theWildUAV dataset prove that the depth estimation method in this paper achieves a good balance between accuracy and model lightweighting. Finally, the detection and distance estimation of early small fires are validated on the dataset UAVFire, demonstrating the effectiveness of the proposed method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".