Real-Time Vehicle Detection Using YOLOv8-Nano for Intelligent Transportation Systems
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
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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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 | 0.000 |
| 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