Automated image-based fire detection and alarm system using edge computing and cloud-based platform
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
To tackle the increasing wildfire challenges, this paper presents an automated image-based fire detection and alarm system utilizing edge computing and a cloud-based platform, specifically designed for urban building fire detection. The system captures both RGB and infrared images from thermal cameras and employs computer vision techniques to detect fire characteristics such as flames and smoke. By integrating edge computing, the system minimizes latency, enhancing the accuracy of fire detection and alarm activation. The cloud platform supports extensive data storage, analysis, and remote monitoring, ensuring hihighly available and scalable data accessibility. The proposed system includes a detailed description of the system architecture design, data collection, and the selection and application of detection algorithms that leverage both RGB and thermal image data for fire detection. Using the campus building and surrounding risk-prone areas as a testbed, the proposed system demonstrated desired fire detection capabilities and a robust solution to quickly identify and respond to fire incidents within the urban area. The proposed system functionalities can be scaled and adapted to other fire risk-prone areas.
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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