Fine-Tuning YOLOv8 for Vehicle Detection: A Deep Learning Approach to Traffic Congestion Monitoring
Bibliographic record
Abstract
Traffic congestion is a crucial challenge in modern urban areas, causing delays, increased emissions, and inefficiencies.This study explores the application of the YOLOv8 model for vehicle detection in the context of traffic congestion monitoring.By fine-tuning YOLOv8 on a vehicle-specific dataset, the model achieved high precision (90.2%), recall (93.6%), and mean Average Precision (mAP50: 97.3%), showcasing its robustness in diverse traffic scenarios.Evaluation metrics, learning curve analysis, and inference results confirm the effectiveness of the fine-tuned model in accurately detecting vehicles, even in complex conditions.However, challenges such as false negatives and limited dataset diversity highlight areas for improvement.As a perspective, real-time video inference is proposed to monitor traffic streams, detect congestion based on vehicle and pedestrian density, and trigger automated decisions.This research establishes a foundation for intelligent traffic monitoring, with potential applications in improving transportation efficiency and reducing urban congestion.
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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 | 0.000 | 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".