Vision-Based Few-Shot Railway Intrusion Detection via Dual-Detector and Contrastive Learning

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 (FSOD) 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 a dual-detector, contrastive learning in 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 intraclass compactness and interclass 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 (SOTA) FSOD methods. It effectively preserves common base intrusion detection performance while efficiently adapting to rare novel intrusions, making it well-suited for railway intrusion detection.