Automatic Flame Detection: Evaluation of Deep Learning Algorithms Using a Custom Thermal Image Dataset
Bibliographic record
Abstract
Abstract Fire safety urgently requires better automatic early fire detection. While vision-based methods are promising, a clear benchmark for deep learning models tailored for this specific area has been lacking. This paper presents the first comprehensive vision-based benchmark of 33 deep learning models explicitly for automatic fire detection. The key novelty is the creation and utilization of a unique, real-world thermal infrared (IR) dataset derived from controlled room fire experiments by NRC Canada. This challenging dataset includes imagery of early-stage and fully developed fires, as well as variations from different test conditions. To assess broader applicability, model generalization was also evaluated using a general dataset (used in pre-training). By rigorously testing these models on both specialized and general datasets using multiple performance metrics (accuracy, speed, reliability, generalization, computational cost), this work establishes the first dedicated benchmark for deep learning in vision-based fire detection. This benchmark provides a novel and crucial resource for researchers to make informed decisions when selecting deep learning models for their specific fire detection applications, ultimately aiming to accelerate innovation and the development of more effective and reliable vision-based fire safety systems.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".