Design and Implementation of a Smart Dual-Stage Fire Crisis Management System Using Raspberry Pi for Safety and Security Applications
Bibliographic record
Abstract
Advances in Internet of Things (IoT) and embedded computing have made it possible to build smarter fire alarms that reduce false triggering, not just detect heat or smoke. This study presents a Raspberry Pi–based fire crisis controller that uses two-stage verification: an infrared flame sensor triggers first, then a Pi Camera runs OpenCV-based image checks to confirm fire before an alert is escalated. Requiring agreement between hardware sensing and vision helps suppress nuisance activations. The prototype integrates the flame sensor, camera, and a piezo buzzer with software for image filtering, database logging, and web-based IoT alerts. In 30 controlled indoor trials, it achieved 98% average detection accuracy and reduced false alarms by 92% compared with a baseline single-sensor flame detector. End-to-end response from ignition to alert activation averaged 9.4 s and stayed under 10 s in all scenarios. After confirmation, the controller sounds the buzzer and posts an alert through the web interface, enabling faster response. Overall, the results show early detection with strong false-alarm suppression using low-cost hardware suitable for residential and small industrial settings. Future work will add smoke and temperature sensing, support offline operation during network outages, and explore RFID tracking of safety equipment to improve on-site coordination.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".