Detecting flashover in a room fire based on the sequence of thermal infrared images using convolutional neural networks
Bibliographic record
Abstract
Flashover phenomena accompanying rapid fire propagation in a room occur when the hot smoke from a fire accumulates in the room's upper part. This phenomenon presents one of the most frightening and challenging situations for firefighters. A typical approach to mitigate and prevent the impact of flashover is to train firefighters to monitor a few common indicators of fire in pre-flashover time, such as moving dark smoke, high heat, and fire rollover. In actual compartment fire events, these pre-flashover indicators are hard to recognize. Furthermore, determination of exact flashover time is difficult by just observing fire activities while there are other vital rescue duties to do by firefighters. Hence, automatic detection and prediction of flashover in real time are of paramount importance to save lives and reduce the cost of damages. Flashover prediction is still an open area of research by fire safety experts. Deep convolutional neural networks are currently dominating the area of computer vision, and these state-of-the-art deep learning models have been successfully used in various applications, including object detection, localization, and segmentation. Unlike previous studies that use RGB images, sensors, and gauges, we utilized the power of deep learning techniques to detect flashover from image sequences captured by thermal infrared (IR) cameras. Our experimental results indicate that not only our proposed approach can detect flashover in IR video data with high precision, but it can detect flashover a few frames before happening. Our technique is a promising approach that can be used in future for flashover prediction in real time.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".