Robustness of Intelligent Transport Systems Employing Computer Vision for the Analysis of Railway Infrastructure Images

Maksim Kulagin; German Yanchenko; Dar'ya Rodina; Aleksey Polegen'ko

doi:10.20295/2412-9186-2025-11-04-313-326

UDC 004.896 UDC 656.25

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ROBUSTNESS OF INTELLIGENT TRANSPORT SYSTEMS EMPLOYING COMPUTER VISION FOR THE ANALYSIS OF RAILWAY INFRASTRUCTURE IMAGES Robustness of Intelligent Transport Systems Employing Computer Vision for the Analysis of Railway Infrastructure Images

АА Аа

Published in Transport automation research · Volume 11, Issue 4 · Pages 313–326 · Rubric: VIABILITY, RELIABILITY, SAFETY

DOI: https://doi.org/10.20295/2412-9186-2025-11-04-313-326

Received: 02.12.2025 Accepted: 03.12.2025 Published: 03.12.2025

Authors

Maksim Kulagin

German Yanchenko

Dar'ya Rodina

Aleksey Polegen'ko

Maksim Kulagin

Academic degree: candidate of technical sciences

Russian University of Transport (MIIT) Associate Professor · Department of Control and Information Security · Russian Federation · Moscow

German Yanchenko

Russian University of Transport (MIIT) Postgraduate Student · Department of Control and Information Security · Russian Federation

Dar'ya Rodina

Russian University of Transport (MIIT) Senior Developer of “Innotech” IT company · Department of Control and Information Security · Russian Federation · Moscow

TI “VTB” Russian Federation

Aleksey Polegen'ko

Russian University of Transport (MIIT) Student · Department of Control and Information Security · Russian Federation

ABSTRACT

With the growing integration of AI systems in the railway sector, it is becoming essential to ensure their robustness against external interference, particularly subtle alterations in input data. This paper analyses the resilience of various neural network architectures, including ResNet18, ResNet50,VisionTransformer, a convolutional neural network, and the GPT-4o multimodal model, which are used for automated fault detection in the analysis of railway infrastructure images. Experiments have been conducted using adversarial disturbances generated via universal noise derived from a set of transformers. Two attack modifications were employed to simulate real-world interference scenarios with limited data availability. The performance of the models has been evaluated on both untainted images and those compromised by overlaid noise. The outcomes indicate that while ResNet50attains maximum accuracy on unmodified data, ViT and GPT-4o demonstrate greater resilience to adversarial disturbances. The research emphasizes the importance of selecting model architectures based on both their accuracy and their robustness against distortions. This paper introduces a method for assessing robustness and offers practical recommendations for developing AI systems designed for application in railway environments where safety is critical.

KEYWORDS

AI systems, railway transport, adversarial attacks, robustness, neural networks, computer image, safety, infrastructure monitoring

Funding

The work was carried out using budgetary funding within the framework of the state assignment dated March 20, 2025 No. 103000012502.

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GOST

Kulagin M. A., Yanchenko G. O., Rodina D. M., Polegen'ko A. I. Robustness of Intelligent Transport Systems Employing Computer Vision for the Analysis of Railway Infrastructure Images // Transport automation research. 2025. no. 4. pp. 313-326. DOI: https://doi.org/10.20295/2412-9186-2025-11-04-313-326. (Date of access 06.11.2026).

MLA

Maksim Kulagin, Yanchenko G. O., Rodina D. M., Polegen'ko A. I.. "Robustness of Intelligent Transport Systems Employing Computer Vision for the Analysis of Railway Infrastructure Images." Transport automation research 11.4 (2025): 313-326. DOI: https://doi.org/10.20295/2412-9186-2025-11-04-313-326.

APA

Kulagin M. A., Yanchenko G. O., Rodina D. M., Polegen'ko A. I. (2025). Robustness of Intelligent Transport Systems Employing Computer Vision for the Analysis of Railway Infrastructure Images. Transport automation research, 11(4), 313-326. https://doi.org/10.20295/2412-9186-2025-11-04-313-326

Chicago

Maksim Kulagin, Yanchenko G. O., Rodina D. M., Polegen'ko A. I.. "Robustness of Intelligent Transport Systems Employing Computer Vision for the Analysis of Railway Infrastructure Images." Transport automation research 11, no. 4 (2025): 313-326. https://doi.org/10.20295/2412-9186-2025-11-04-313-326.

WIKI

{{cite journal | author = Kulagin M. A., Yanchenko G. O., Rodina D. M., Polegen'ko A. I. | title = Robustness of Intelligent Transport Systems Employing Computer Vision for the Analysis of Railway Infrastructure Images | journal = Transport automation research | year = 2025 | volume = 11 | issue = 4 | pages = 313-326 | doi = 10.20295/2412-9186-2025-11-04-313-326 }}

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ROBUSTNESS OF INTELLIGENT TRANSPORT SYSTEMS EMPLOYING COMPUTER VISION FOR THE ANALYSIS OF RAILWAY INFRASTRUCTURE IMAGES Robustness of Intelligent Transport Systems Employing Computer Vision for the Analysis of Railway Infrastructure Images

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