Security Surveillance Using UAVs and Embedded Systems in Industrial Areas
Bibliographic record
Abstract
This paper proposes a novel security surveillance Unmanned Aerial Vehicle (UAV) that can handle security in large industrial areas with increased surveillance efficiency.Our basic idea is that Unmanned Aerial Vehicle sighting can be treated as a motion detection problem in the surveillance area by detecting position and type simultaneously when the Unmanned Aerial Vehicle flies in the 360° detection area.To reach our target, this paper proposes a mathematical approach based on camera calibration, ordinal distortion correction, and three-dimensional reconstruction that can help us determine the exact position of a moving object in the monitored area.It is also important to recognize movements and their character and to determine their position on the ground, all of this must be done in Real-time with short processing times.The outcomes of our study demonstrate that system processing average duration and processing system consumption have slightly decreased with the utilization of the Raspberry Pi+VPU system compared to alternatives such as the Jetson Nano, Raspberry Pi 4 boards, clusters, and personal computers.This underscores the effectiveness of our proposed system in terms of processing efficiency and resource utilization.
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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 | 0.000 |
| 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) | 0.001 | 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".