Wildfire Flame and Smoke Detection Using Static Image Features and Artificial Neural Network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
If forest fires are not contained quickly, they can spread wide very fast and cause devastating environmental, social and economic damages. The best method to minimize wildfire loss is to be able to detect it in its early stages for rapid containment and suppression. Fire comes with some distinguishable signatures such as flame, smoke and heat that can be used for early detection using computer vision based remote sensing techniques. Each signature has its own merits and demerits that vary under different environmental conditions and circumstances. Therefore, it is not always enough to form a detection algorithm based on a single signature. Keeping that in mind, this paper presents a novel algorithm that is capable of detecting both flame and smoke from a single image using block-based color features, texture features and a single artificial neural network (ANN). Such an algorithm is capable of providing reliable, rapid and continuous detection under any circumstances and can be incorporated into the existing unmanned aerial vehicle (UAV) based fire monitoring system.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it