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<p>Monitoring railway fasteners is essential for track safety. However, the development of intelligent inspection systems is hindered by the scarcity of defective fastener images and the pronounced imbalance between defective and non-defective classes. Conventional generative approaches, such as GAN- and diffusion-based models, can increase data volume but often incur high computational cost, unstable convergence, or produce samples lacking structural fidelity, limiting their suitability for engineering deployment. This work proposes the Domain-Adaptive NeuroGenerative Framework (DA-NGF), a lightweight few-shot generative model tailored to rail fastener inspection. The framework leverages compact latent modeling, orientation-invariant self-supervision, and defect-aware attention to synthesize structurally consistent fastener images from extremely limited real samples. Synthetic samples are evaluated using a separate downstream classifier (FastenerNet + +), enabling quality validation without coupling generation and classification pipelines. Experimental results show that augmenting real data with DA-NGF synthetic samples improves downstream defect recognition accuracy by 21.13% and precision by 22.53%, with corresponding gains in recall and F1-score. The model executes efficiently on CPU hardware, underscoring its suitability for low-resource deployment.</p>

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DA-NGF: A Domain-Adaptive Neurogenerative Framework for Few-Shot Railway Fastener Defect Synthesis and Transferable Representation Learning

  • 期刊论文

  • 发表日期：

    2026年1月7日

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  • Qasim Zaheer,
  • Haleema Ehsan,
  • Weidong Wang,
  • Syed Faizan Hussain Shah,
  • Chengbo Ai,
  • Jin Wang,
  • Shi Qiu

摘要

Monitoring railway fasteners is essential for track safety. However, the development of intelligent inspection systems is hindered by the scarcity of defective fastener images and the pronounced imbalance between defective and non-defective classes. Conventional generative approaches, such as GAN- and diffusion-based models, can increase data volume but often incur high computational cost, unstable convergence, or produce samples lacking structural fidelity, limiting their suitability for engineering deployment. This work proposes the Domain-Adaptive NeuroGenerative Framework (DA-NGF), a lightweight few-shot generative model tailored to rail fastener inspection. The framework leverages compact latent modeling, orientation-invariant self-supervision, and defect-aware attention to synthesize structurally consistent fastener images from extremely limited real samples. Synthetic samples are evaluated using a separate downstream classifier (FastenerNet + +), enabling quality validation without coupling generation and classification pipelines. Experimental results show that augmenting real data with DA-NGF synthetic samples improves downstream defect recognition accuracy by 21.13% and precision by 22.53%, with corresponding gains in recall and F1-score. The model executes efficiently on CPU hardware, underscoring its suitability for low-resource deployment.