Active Learning for Multi-Class Vehicle Categorization and Traffic Analysis in complex environments
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
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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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 it