An IoT enabled Artifact Protection System for Museum using Computer Vision
Bibliographic record
Abstract
Currently, museums lack preventive security measures, leading to the theft of precious artifacts. Safeguarding artifacts and any sacred items in museums is a challenging job, as they need to be secured while at the same time being accessible to all visitors. This calls for an integrated security system to prevent theft and decrease crimes involving artifacts. This paper proposes a novel IoT-enabled, computer vision-based, holistic approach for protecting artifacts in museums. A camera functions as the imaging device that is employed to detect the movement of any artifact from a predetermined position, while the Raspberry Pi 4B manages the processing and operations of the system. This system further utilizes an image processing technique, i.e., the Haar Cascade algorithm and OpenCV, to detect artifact movement. In addition, the device will also record the photos of any authorized or unauthorized individuals anytime it detects any movement of the artifact. The system has produced a high accuracy of 93.33% and high precision of 96.29%, indicating a higher level of reliability with very few false alarms. This method will serve as an efficient mechanism for protecting artifacts at a reduced cost. The proposed system can be implemented at any kind of museum or cultural site.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".