Master-Slave Convolutional Deep Architecture for Vehicle Identification and Type Classification
Bibliographic record
Abstract
Video surveillance of road traffic plays an important role in highway safety and is an important application of intelligent transportation systems.One of the basic applications of intelligent transport systems is the detection and classification of vehicle types.The major problems encountered by these systems are the significant similarity between the vehicles, frequent occlusions on the highway, and low resolution of the surveillance cameras.This paper proposes a novel convolutional neural network architecture called master-slave convolutional deep architecture for vehicle detection and type classification.The basic concept of this architecture is twofold: a.The sequential operation of the two networks where the slave network only works if the master network detects vehicles in the road scene will allow a considerable reduction in the search area for vehicles.It will induce a significant reduction in processing time.b.A combination of deep-shallow neural networks allows the system to share the knowledge gained from the vehicles on two networks.The first (master) shallow learns the shape of vehicles while the second (slave) is responsible for learning all the details of vehicles to distinguish the different classes.The experimental results, performed on 3200 images, have shown that the favorable performance of the proposed CNN architecture allowed us to achieve successful detection with TPR of 92% and TNR of 95% and vehicle type classification with a considerable mean average precision of 93.38% where cars classification gives the highest rate (98.63%).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".