[1] A CATALANO, FA BRUNO, C GALLIANO et al. An optical fiber intrusion detection system for railway security. Sensors and Actuators A： Physical, 253, 91-100(2017).

[2] F J ALVAREZ, J URENA, M MAZO et al. Ultrasonic Sensor System for Detecting Falling Objects on Railways, 866-871(17).

[3] [3] 3王瑞， 李霄峰， 史天运， 等. 基于视频深度学习的铁路周界入侵检测算法研究［J］. 交通运输系统工程与信息， 2020， 20（2）： 61-68.WANGR， LIX F， SHIT Y， et al. Railway perimeter intrusion detection algorithms based on video deep learning［J］. Journal of Transportation Systems Engineering and Information Technology， 2020， 20（2）： 61-68. （in Chinese）

[4] [4] 4陈莹， 朱宇. 模态自适应权值学习机制下的多光谱行人检测网络［J］. 光学 精密工程， 2020， 28（12）： 2700-2709. doi: 10.37188/OPE.20202812.2700CHENY， ZHUY. Multispectral pedestrian detection network under modal adaptive weight learning mechanism［J］. Opt. Precision Eng.， 2020， 28（12）： 2700-2709. （in Chinese）. doi: 10.37188/OPE.20202812.2700

[5] [5] 5郭保青， 王宁. 基于改进深度卷积网络的铁路入侵行人分类算法［J］. 光学 精密工程， 2018， 26（12）： 3040-3050. doi: 10.3788/ope.20182612.3040GUOB Q， WANGN. Pedestrian intruding railway clearance classification algorithm based on improved deep convolutional network［J］. Opt. Precision Eng.， 2018， 26（12）： 3040-3050. （in Chinese）. doi: 10.3788/ope.20182612.3040

[6] [6] 6何文玉， 杨杰， 张天露. 基于深度学习的轨道异物入侵检测算法［J］. 计算机工程与设计， 2020， 41（12）： 3376-3383.HEW Y， YANGJ， ZHANGT L. Orbital foreign object intrusion detection algorithm based on deep learning［J］. Computer Engineering and Design， 2020， 41（12）： 3376-3383. （in Chinese）

[7] [7] 7宋闯，赵佳佳，王康等.面向智能感知的小样本学习研究综述［J］.航空学报，2020，41（S1）： 15-28.SONGCH， ZHAOJ J， WANGK， et al. A survey of few shot learning based on intelligent perception［J］. Acta Aeronautica et Astronautica Sinica， 2020， 41（S1）：15-28.

[8] J SNELL, Y K SWERSK, R ZEMEL. Prototypical net-works for few-shot learning, 4077-4087(2017).

[9] F SUNG, Y YANG, L ZHANG et al. Learning to compare:Relation network for few-shot learning, 1199-1208(2018).

[10] C Y XU, J Z SHEN, X DU. A method of few-shot network intrusion detection based on meta-learning framework. IEEE Transactions on Information Forensics and Security, 15, 3540-3552(2020).

[11] A ALFASSY, L KARLINSKY, A AIDES et al. LaSO： Label-Set Operations Networks for Multi-Label Few-Shot Learning, 6541-6550(15).

[12] C ZHANG, Y J CAI, G S LIN et al. DeepEMD： Few-Shot Image Classification with Differentiable Earth Mover's Distance and Structured Classifiers, 12200-12210(13).

[13] K M HE, X Y ZHANG, S Q REN et al. Deep Residual Learning for Image Recognition, 770-778(27).

[14] J HU, L SHEN, G SUN. Squeeze-and-Excitation Networks, 7132-7141(18).

[15] J PARK, J Y LEE et al. CBAM： Convolutional Block Attention Module, 3-19(2018).

[16] Y D WEN, K P ZHANG, Z F LI et al. A Discriminative Feature Learning Approach for Deep Face Recognition, 499-515(2016).

[17] C FINN, P ABBEEL, S LEVINE. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, 1126-1135(11).

[18] O VINYALS, C BLUNDELL, T LILLICRAP et al. Matching Networks for One Shot Learning, 3637-3645(2016).

[19] W CHEN, Y LIU, Z KIRA et al. A closer look at few-shot classification, 2019(1904).