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Generative Model-Driven Synthetic Training Image Generation: An Approach to Cognition in Railway Defect Detection

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Abstract

Recent advancements in cognitive computing, through the integration of artificial intelligence (AI) techniques, have facilitated the development of intelligent cognitive systems (ICS). This benefits railway defect detection by enabling ICS to emulate human-like analysis of defect patterns in image data. Although visual defect classification based on convolutional neural networks (CNN) has achieved decent performance, the scarcity of large datasets for railway defect detection remains a challenge. This scarcity stems from the infrequent nature of accidents that result in defective railway parts. Existing research efforts have addressed the challenge of data scarcity by exploring rule-based and generative data augmentation approaches. Among these approaches, variational autoencoder (VAE) models can generate realistic data without the need for extensive baseline datasets for noise modeling. This study proposes a VAE-based synthetic image generation technique for training railway defect classifiers. Our approach introduces a modified regularization strategy that combines weight decay with reconstruction loss. Using this method, we created a synthetic dataset for the Canadian Pacific Railway (CPR), consisting of 50 real samples across five classes. Remarkably, our method generated 500 synthetic samples, achieving a minimal reconstruction loss of 0.021. A visual transformer (ViT) model, fine-tuned using this synthetic CPR dataset, achieved high accuracy rates (98–99%) in classifying the five railway defect classes. This research presents an approach that addresses the data scarcity issue in railway defect detection, indicating a path toward enhancing the development of ICS in this field.

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Data Availability

We have signed a business agreement with the data provider. Accordingly, the datasets analyzed during the current study are not publicly available. However, we have consent for publishing the result.

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Funding

The authors extend their appreciation to the researchers supporting project number (RSP2024R32), King Saud University, Riyadh, Saudi Arabia. This research is also supported in part by collaborative research funding from the National Program Office under the National Research Council of Canada’s Artificial Intelligence for Logistics Program. The project ID is AI4L-123.

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Authors and Affiliations

  1. The University of Ottawa, Ottawa, ON, Canada

    Rahatara Ferdousi, Fedwa Laamarti & Abdulmotaleb El Saddik

  2. National Research Council Canada, Ottawa, ON, Canada

    Chunsheng Yang

  3. School of Computing, Queen’s University, Kingston, ON, Canada

    M. Anwar Hossain

  4. Computer Vision Department, Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates

    Fedwa Laamarti & Abdulmotaleb El Saddik

  5. Department of Software Engineering, College of Computer and Information Sciences, King Saud University, 12372, Riyadh, Saudi Arabia

    M. Shamim Hossain

  6. Institute of Artificial Intelligence, Guangzhou University, Guangzhou, 510006, China

    Chunsheng Yang

Authors
  1. Rahatara Ferdousi
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  2. Chunsheng Yang
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  3. M. Anwar Hossain
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  4. Fedwa Laamarti
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  5. M. Shamim Hossain
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  6. Abdulmotaleb El Saddik
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Contributions

Rahatara Ferdousi: Conceptualization, Methodology, Writing—Original Draft Preparation, Visualization, Validation, Software. Chunsheng Yang: Review & Data Collection, Validation, Project administration. M. Anwar Hossain: Writing—Review & Editing, Validation, Supervision. Fedwa Laamarti: Writing—Review & Editing. Abdulmotaleb El Saddik & M. Shamim Hossain: Discussion, Review, Funding acquisition, Supervision.

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Correspondence to Rahatara Ferdousi or M. Shamim Hossain.

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Ferdousi, R., Yang, C., Hossain, M.A. et al. Generative Model-Driven Synthetic Training Image Generation: An Approach to Cognition in Railway Defect Detection. Cogn Comput (2024). https://doi.org/10.1007/s12559-024-10283-3

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