Vision-Based Few-Shot Railway Intrusion Detection via Dual-Detector and Contrastive Learning
Bibliographic record
Abstract
With the rapid advancement of rail transit, railway intrusion detection has become a crucial and indispensable technology for ensuring the safe operation of trains. Current mainstream railway intrusion detection methods are based on deep learning and general object detection frameworks. However, they rely on large-scale, high-cost annotated datasets, leading to expensive data collection and poor performance in data-scarce railway scenarios. Meanwhile, few-shot object detection methods often generalize poorly to novel classes and suffer from catastrophic forgetting of base classes. To address these issues, we leverage a visible-light camera as the vision sensor and propose a few-shot railway intrusion detection method based on Dual Detector, Contrastive Learning within Novel Classes (CLNC), and an Efficient Fine-Tuning Framework. The Dual Detector design decouples the detection of base and novel classes, mitigating catastrophic forgetting, while the CLNC module enhances intra-class compactness and inter-class separability, improving generalization to novel classes. Additionally, the Efficient Fine-Tuning Framework optimizes module collaboration, further enhancing detection accuracy. Extensive experiments on the self-constructed few-shot railway intrusion dataset (FSRI2024), collected using a visible-light camera, demonstrate that the proposed G-FSRD achieves better performance compared to state-of-the-art few-shot object detection methods. It effectively preserves common base intrusions detection performance while efficiently adapting to rare novel intrusions, making it well-suited for railway intrusion detection.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".