Utilising Deep Convolutional Neural Networks for Classifying Fire Disasters Through Surveillance: An Indoor and Outdoor Perspective to Predict Man-Made or Natural Disaster
Bibliographic record
Abstract
Disasters, unpredictable events inflicting substantial harm to human lives and property, are categorized broadly into natural and man-made occurrences.Fires, in particular, pose significant threats due to their hazardous impact and the challenges associated with early detection and origin determination.This study narrows its focus to fires, aiming to predict their onset and distinguish between man-made and natural causes.Over recent decades, traditional algorithms have been employed to predict fire events; however, this work adopts a novel approach, utilizing deep neural networks in conjunction with surveillance systems.The proposed model not only predicts the onset of a fire but also identifies its likely cause and location, specifically differentiating between indoor and outdoor fires.Furthermore, the model maintains the integrity of sensitive details present in the original images, an essential consideration for privacy and safety.The model was trained and tested on realtime fire datasets, resulting in an impressive accuracy of 97.44% in predicting the nature of the fire and classifying its location.This work thus contributes significantly to disaster management efforts by enabling early fire detection, facilitating rapid response, and ultimately safeguarding human lives and property.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".