AI-Powered Early Fire Detection: Refining Datasets and Deploying YOLOv11 for Improved Accuracy
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
Early fire detection is critical for safeguarding lives, minimizing property damage, and ensuring public safety in various environments, including residential areas, offices, and natural landscapes. The integration of deep learning techniques in fire detection enhances emergency response by enabling rapid identification and alerting relevant authorities. This study presents a novel approach by creating and labeling two distinct YOLO deep-learning model variants trained on a large dataset compiled from multiple existing research efforts. These models achieved a precision of 92% on the merged dataset, demonstrating significant improvements compared to the most recent works in the field. Additionally, this study introduces a web-based deployment framework, allowing real-time utilization of the trained models in practical applications. By improving the speed and accuracy of fire detection, the proposed system contributes to public safety initiatives, offering a scalable and efficient approach for automated fire prevention strategies in diverse settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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