基于度量元学习的铁路小样本入侵目标检测方法

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

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%。

Object intrusion is among the primary causes of railway accidents.Typically,traditional deeplearning 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%.