Active Learning for Multi-Class Vehicle Categorization and Traffic Analysis in complex environments
Bibliographic record
Abstract
This paper presents a novel approach designed for the study of vehicles, with a primary focus on enhancing the assessment of goods and their value. The framework aims to improve the comprehension of vehicular traffic dynamics on municipalities, thereby enabling improved route planning and inspection strategies. Our proposed closed-loop system integrates deep learning, conventional image processing and computer vision to detect, track, count, timestamp, and estimate the direction of travel for vehicles, thus laying the groundwork for in-depth traffic flow analysis and optimization. The proposed framework incorporates a unique data processing mechanism within a crowdsourcing environment, enhancing the scalability of our system. For multiclass object detection we proposed a single stage and two-stage pipelines using YOLOv8, YOLOv6, YOLOv5 and RT-DETR-LR models. Our tracking stage computes cumulative average confidence scores per estimated class over a vehicle’s lifespan, enhancing class prediction robustness. Our method achieved 0.891 mAP score with data augmentation strategies. Experimental results demonstrate the effectiveness, efficiency, and robustness of the proposed system on challenge scenes and adaptability with active learning for vehicular analysis.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".