Research on Parking Lot Vehicle Counting Based on Simplified YOLOv3
Bibliographic record
Abstract
Aiming at the problems of low efficiency and high error rate in traditional parking lot vehicle counting which relies on manual inspection, this paper proposes a vehicle detection and counting method based on the simplified YOLOv3, specially adapted to the parking lot monitoring scenarios with fixed viewing angles and no occlusion. By simplifying the network structure of YOLOv3, the method improves the operation speed while ensuring the detection accuracy, thus meeting the real-time counting requirements. Experimental results show that the vehicle detection accuracy of this method reaches 95.2% on the self-built parking lot dataset, the counting error is controlled within 3%, and the operation efficiency is increased by 40% compared with the original YOLOv3 model. It can effectively realize the automatic and accurate counting of vehicles in parking lots, providing technical support for the intelligent upgrading of parking lot management systems.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".