Detection of Unusual Targets in Traffic Images Based on One-Class Extreme Machine Learning
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 traffic image target detection, unusual targets like a running dog has not been paid sufficient attention. The mature detection methods for general targets cannot be directly applied to detect unusual targets, owing to their high complexity, poor feature expression ability, and requirement for numerous manual labels. To effectively detect unusual targets in traffic images, this paper proposes a multi-level semi-supervised one-class extreme learning machine (ML-S2OCELM). Specifically, the extreme learning machine (ELM) was chosen as the basis to develop a classifier, whose variables could be calculated directly at the cost of limited computing resources. The hypergraph Laplacian array was employed to improve the depiction of data smoothness, making semi-supervised classification more accurate. Furthermore, a stack auto-encoder (AE) was introduced to implement a multi-level neural network (NN), which can extract discriminative eigenvectors with suitable dimensions. Experiments show that the proposed method can efficiently screen out traffic images with unusual targets with only a few positive labels. The research results provide a time-efficient, and resource-saving instrument for feature expression and target detection.
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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