Real-Time Vehicle Detection Using YOLOv8-Nano for Intelligent Transportation Systems
Bibliographic record
Abstract
In recent years, deep learning models have seen extensive use in various domains, with the YOLO algorithm family emerging as a prominent player.YOLOv5, known for its real-time object detection capabilities and high accuracy, has been widely embraced in transportationrelated research.However, the introduction of YOLOv8 in early 2023 signifies a significant leap forward in object detection technology.Despite its potential, the literature on YOLOv8 remains relatively scarce, leaving room for exploration and adoption in research.This study pioneers real-time vehicle detection using the YOLOv8 algorithm.An in-depth analysis of YOLOv8n, the smallest scale model within the YOLOv8 series, was conducted to assess its suitability for real-time scenarios, particularly in Intelligent Transportation Systems (ITS).To reinforce its real-time capabilities, a parametric analysis covering image processing time, detection sensitivity, and input image characteristics was performed.To optimize model performance, a training dataset was created through flight tests using a custom autonomous drone, encompassing various vehicle variations.This ensures that the model excels in recognizing diverse motor vehicle configurations.The results reveal that even this compact sub-model achieves an impressive detection accuracy rate exceeding 80%.The study establishes that YOLOv8n, evaluated for the first time in ITS applications, effectively serves as an object detector for real-time smart traffic management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".