Real-Time Pedestrian Detection System Based on YOLOv5-tiny
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
Real-time pedestrian detection, as a key technology in the field of computer vision, has broad application demands in intelligent surveillance, autonomous driving, robot navigation, and other areas. To address the problem that high-computational-power models are difficult to deploy on edge devices, this paper proposes a real-time pedestrian detection scheme based on the lightweight YOLOv5-tiny model. The study uses a pedestrian subset of the COCO dataset for model training, optimizes the anchor box dimensions through the K-means clustering algorithm to adapt to pedestrian target characteristics, and tests the model performance on ordinary CPU and GPU environments. Experimental results show that the optimized model can achieve a detection speed of 23.6 FPS with a recall rate of 82.3% on the Intel Core i7-10700 CPU; on the NVIDIA GTX 1650 GPU, the frame rate increases to 45.2 FPS and the recall rate rises to 84.7%, which can meet the real-time and detection accuracy requirements in low-computational-power scenarios.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.009 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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