Railway few-shot intruding objects detection method with metric meta learning

GUO Baoqing; ZHANG Defen

doi:10.37188/OPE.20233112.1816

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Railway few-shot intruding objects detection method with metric meta learning

Information Sciences | 更新时间：2023-07-17

- Railway few-shot intruding objects detection method with metric meta learning
- Optics and Precision Engineering Vol. 31, Issue 12, Pages: 1816-1826(2023)
- 作者机构：
  
  1.北京交通大学机械与电子控制工程学院，北京 100044
  2.北京交通大学智慧高铁系统前沿科学中心，北京 100044
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20233112.1816
  CLC： U298;TP394.1
- Received：14 June 2022，
  
  Revised：06 August 2022，
  
  Published：25 June 2023
- 稿件说明：
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郭保青,张德芬.基于度量元学习的铁路小样本入侵目标检测方法[J].光学精密工程,2023,31(12):1816-1826.

GUO Baoqing,ZHANG Defen.Railway few-shot intruding objects detection method with metric meta learning[J].Optics and Precision Engineering,2023,31(12):1816-1826.
郭保青,张德芬.基于度量元学习的铁路小样本入侵目标检测方法[J].光学精密工程,2023,31(12):1816-1826. DOI： 10.37188/OPE.20233112.1816.

GUO Baoqing,ZHANG Defen.Railway few-shot intruding objects detection method with metric meta learning[J].Optics and Precision Engineering,2023,31(12):1816-1826. DOI： 10.37188/OPE.20233112.1816.

摘要

异物入限是导致铁路安全事故频发的主要原因之一，传统深度学习需要大量训练样本进行网络训练，但铁路场景中入侵样本很少且难于获取。本文提出了基于改进度量元学习的铁路小样本异物入侵检测方法。为了让入侵目标的特征表征在分类时发挥更大作用，提出了基于通道注意力机制的特征提取网络；为解决样本数量不足时个别样本在特征空间中产生偏离的问题，提出了一种基于类中心微调的网络用于类别中心的修正；同时，基于center loss与交叉熵构建了中心相关损失函数用于小样本网络训练，提升特征空间中同类别特征分布的紧凑性。在公共数据集miniImageNet上与经典小样本学习模型中最优的相比，本文算法在5-way 5-shot设置下图像分类准确率提升了7.31%。在铁路入侵小样本数据集的5-way 5-shot消融实验表明：本文提出的通道注意力机制（Channel Attention Mechanism，CAM）和中心相关损失函数分别提升0.86%和1.91%的检测精度；提出的类中心微调和预训练方法对检测精度的提升效果更明显，分别达到3.05%和6.70%，上述模块综合应用的提升效果达到了7.90%。

Abstract

Object intrusion is among the primary causes of railway accidents. Typically， traditional deep-learning methods require numerous samples for network training； however， intrusion samples in railway settings are scarce and difficult to obtain. Thus， in this paper， a railway few-shot intruding-object detection method based on an improved metric meta-learning network is proposed. To better exploit the features of intruding objects during classification， a feature-extraction network based on the channel attention mechanism is proposed. A network based on fine-tuning of the class center is proposed for class-center correction to solve the problem of individual samples deviating in the feature space of insufficient samples. Additionally， a central correlation loss function based on the center loss and cross entropy is constructed for few-shot network training to improve the compactness of the same-class feature distribution in the feature space. In experiments on a public few-shot dataset called miniImageNet， the accuracy of the proposed method is 7.31% higher than the optimal accuracy of the classical few-shot learning model. In five-way five-shot ablation experiments using a railway dataset， the proposed channel attention mechanism and center-related loss function increase the mean average precision （mAP） by 0.86% and 1.91%， respectively. Additionally， the center fine-tuning and pretraining increase the mAP by 3.05% and 6.70%， respectively， and the total mAP improvement is 7.90%.

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references

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