Aiming at the serious threat to train safety posed by the railway intrusion obstacles, while the general object detection methods based on deep learning struggle to break the barrier of data-driven training, the few-shot object detection methods have weak detection ability and low robustness for multi-scale obstacles in complex railway environments, this paper presents a high robust multi-scale fewshot railway intrusion obstacles detection model ( FRL-Net). The model utilizes the meta-learning strategy to capture rich feature information by designing the multi-scale few-shot obstacle feature extraction module, which can enhance the model′s ability to express the features of few-sample objects at different scales. The precise reweighting module is used for optimizing the meta-feature at different scales, and the few-shot railway obstacle detection optimization module is proposed to further enhance the few-shot railway obstacle detection performance of the model. The experimental results show that the proposed model achieves the mAP of 81. 8% in the 7-way 30-shot few-shot railway obstacle detection task, which is 3. 2% higher than that of FSRW. It is more suitable for detecting few-shot multi-scale railway obstacles in actual railway environments.