A Comparative Analysis of Multi-Label Deep Learning Classifiers for Real-Time Vehicle Detection to Support Intelligent Transportation Systems
Why this work is in the frame
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Bibliographic record
Abstract
An Intelligent Transportation System (ITS) is a vital component of smart cities due to the growing number of vehicles year after year. In the last decade, vehicle detection, as a primary component of ITS, has attracted scientific attention because by knowing vehicle information (i.e., type, size, numbers, location speed, etc.), the ITS parameters can be acquired. This has led to developing and deploying numerous deep learning algorithms for vehicle detection. Single Shot Detector (SSD), Region Convolutional Neural Network (RCNN), and You Only Look Once (YOLO) are three popular deep structures for object detection, including vehicles. This study evaluated these methodologies on nine fully challenging datasets to see their performance in diverse environments. Generally, YOLO versions had the best performance in detecting and localizing vehicles compared to SSD and RCNN. Between YOLO versions (YOLOv8, v7, v6, and v5), YOLOv7 has shown better detection and classification (car, truck, bus) procedures, while slower response in computation time. The YOLO versions have achieved more than 95% accuracy in detection and 90% in Overall Accuracy (OA) for the classification of vehicles, including cars, trucks and buses. The computation time on the CPU processor was between 150 milliseconds (YOLOv8, v6, and v5) and around 800 milliseconds (YOLOv7).
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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 | 0.002 |
| 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