Impact of Face Detection Algorithms on UAV-Based Real-Time Face Recognition Systems
Bibliographic record
Abstract
Unmanned Aerial Vehicles (UAVs) equipped with real-time face recognition systems play a crucial role in security and surveillance applications, particularly for intruder detection in high-security environments. However, the efficiency of these systems heavily depends on the performance of the face detection algorithm, which serves as the first stage in the face recognition pipeline. This paper evaluates the impact of three state-of-the-art face detection algorithms—UWS-YOLO, YOLOv7, and RetinaFace—on the real-time performance of a UAV-based face recognition system. Experimental results show that UWS-YOLO achieves the best balance between speed and accuracy, with an inference time of 12.5 ms, an Intruder Detector AI processing time of 26.8 ms, and an overall face recognition pipeline execution time of 48.3 ms, outperforming YOLOv7 (59.3 ms) and RetinaFace (67.9 ms). These findings highlight the importance of minimising face detection latency to ensure real-time UAV-based surveillance operations.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".