Intelligent Detection of Surface Defects in High-Speed Railway Ballastless Track Based on Self-Attention and Transfer Learning

IF 4.6 2区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Wenlong Ye, Juanjuan Ren, Chen Li, Wengao Liu, Zeyong Zhang, Chunfang Lu
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引用次数: 0

Abstract

The detection of ballastless track surface (BTS) defects is a prerequisite for ensuring the safe operation of high-speed railways. Traditional convolutional neural networks fail to fully exploit contextual information and lack global pixel representations. The extensive stacking of convolutions leads deep learning models to play a black-box detection role, lacking interpretability. Due to the current lack of sufficient high-quality surface data for ballastless tracks, it is a severe constraint on the accurate identification of the substructure state in high-speed railways. This paper proposes an intelligent detection method for BTS defects named TrackNet based on self-attention and transfer learning. The method enhances the fusion ability of global features of BTS defects using multihead self-attention. The model’s dependence on extensive defect data is reduced by transferring knowledge from large-scale publicly available datasets. Experimental results demonstrate that compared to advanced Swin Transformer model results, the TrackNet model achieves improvements in average accuracy and F1-score by 5.15% and 5.16%, respectively, on limited test data. The TrackNet model visualizes the decision regions of the model in identifying BTS defects, revealing the black-box recognition mechanism of deep learning models. This research performs engineering applications and provides valuable insights for the multiclass recognition of BTS defects in high-speed railways.

Abstract Image

基于自我关注和迁移学习的高速铁路无砟轨道表面缺陷智能检测
检测无砟轨道表面(BTS)缺陷是确保高速铁路安全运行的先决条件。传统的卷积神经网络无法充分利用上下文信息,也缺乏全局像素表征。卷积的大量堆叠导致深度学习模型扮演着黑箱检测的角色,缺乏可解释性。由于目前缺乏足够的高质量无砟轨道表面数据,严重制约了高速铁路下部结构状态的准确识别。本文提出了一种基于自我注意和迁移学习的基站缺陷智能检测方法,命名为 TrackNet。该方法利用多头自注意增强了基站缺陷全局特征的融合能力。通过转移大规模公开数据集的知识,该模型降低了对大量缺陷数据的依赖。实验结果表明,与先进的 Swin Transformer 模型结果相比,在有限的测试数据上,TrackNet 模型的平均准确率和 F1 分数分别提高了 5.15% 和 5.16%。TrackNet 模型将模型识别基站缺陷的决策区域可视化,揭示了深度学习模型的黑箱识别机制。该研究具有工程应用价值,为高速铁路基站缺陷的多类识别提供了宝贵的启示。
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来源期刊
Structural Control & Health Monitoring
Structural Control & Health Monitoring 工程技术-工程:土木
CiteScore
9.50
自引率
13.00%
发文量
234
审稿时长
8 months
期刊介绍: The Journal Structural Control and Health Monitoring encompasses all theoretical and technological aspects of structural control, structural health monitoring theory and smart materials and structures. The journal focuses on aerospace, civil, infrastructure and mechanical engineering applications. Original contributions based on analytical, computational and experimental methods are solicited in three main areas: monitoring, control, and smart materials and structures, covering subjects such as system identification, health monitoring, health diagnostics, multi-functional materials, signal processing, sensor technology, passive, active and semi active control schemes and implementations, shape memory alloys, piezoelectrics and mechatronics. Also of interest are actuator design, dynamic systems, dynamic stability, artificial intelligence tools, data acquisition, wireless communications, measurements, MEMS/NEMS sensors for local damage detection, optical fibre sensors for health monitoring, remote control of monitoring systems, sensor-logger combinations for mobile applications, corrosion sensors, scour indicators and experimental techniques.
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