Selection of Artificial Intelligence Algorithms in Computer Vision Tasks for Detection and Localization of Objects on Railways

Leonid Baranov; Maksim Kulagin; German Yanchenko

doi:10.20295/2412-9186-2026-12-02-128-137

UDC 004.8 UDC 004.93 UDC 656.2

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SELECTION OF ARTIFICIAL INTELLIGENCE ALGORITHMS IN COMPUTER VISION TASKS FOR DETECTION AND LOCALIZATION OF OBJECTS ON RAILWAYS Selection of Artificial Intelligence Algorithms in Computer Vision Tasks for Detection and Localization of Objects on Railways

АА Аа

Published in Transport automation research · Volume 12, Issue 2 · Pages 128–137 · Rubric: INTELLIGENT CONTROL SYSTEMS

DOI: https://doi.org/10.20295/2412-9186-2026-12-02-128-137

Received: 23.06.2026 Accepted: 23.06.2026 Published: 23.06.2026

Authors

Leonid Baranov

Maksim Kulagin

German Yanchenko

Leonid Baranov

Academic degree: doctor of technical sciences

Science rang: professor

Russian University of Transport (MIIT) Russian Federation · Moscow

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

ABSTRACT

this article addresses the problem of selecting and substantiating neural network architectures for computer vision systems in railway transport. In the context of digital transformation within the industry, the development of reliable intelligent decision support systems capable of real-time operation has become a critical priority. This study presents a comparative analysis of the effectiveness of modern convolutional neural networks and emerging transformer-based architectures for the semantic segmentation of key railway infrastructure objects. The research is based on a specialized dataset comprising 8,203 images captured by onboard locomotive cameras under diverse weather and lighting conditions. Five artificial intelligence models were evaluated: U-Net, U-Net++, DeepLabV3+, MAnet, and SegFormer. Performance assessment was conducted using criteria including the Mean Intersection over Union (mIoU) metric, inference speed, and analysis of Type I (false alarm) and Type II (missed detection) error probabilities. Experimental results demonstrate that the U-Net++ architecture with a DenseNet-121 encoder delivers the most balanced performance, minimizing critical missed detections while maintaining high localization accuracy. The findings and proposed evaluation criteria provide a structured framework for the substantiated selection of machine vision algorithms in the design of autonomous control systems and intelligent decision support systems for operators of track maintenance machinery.

KEYWORDS

semantic segmentation, convolutional neural networks, transformers, computer vision, railway infrastructure, artificial intelligence

Funding

This work was carried out under state assignment No. 103-00001-26-00 dated January 15, 2026, funded by the state budget.

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GOST

Baranov L. A., Kulagin M. A., Yanchenko G. O. Selection of Artificial Intelligence Algorithms in Computer Vision Tasks for Detection and Localization of Objects on Railways // Transport automation research. 2026. no. 2. pp. 128-137. DOI: https://doi.org/10.20295/2412-9186-2026-12-02-128-137. (Date of access 26.09.2026).

MLA

Leonid Baranov, Kulagin M. A., Yanchenko G. O.. "Selection of Artificial Intelligence Algorithms in Computer Vision Tasks for Detection and Localization of Objects on Railways." Transport automation research 12.2 (2026): 128-137. DOI: https://doi.org/10.20295/2412-9186-2026-12-02-128-137.

APA

Baranov L. A., Kulagin M. A., Yanchenko G. O. (2026). Selection of Artificial Intelligence Algorithms in Computer Vision Tasks for Detection and Localization of Objects on Railways. Transport automation research, 12(2), 128-137. https://doi.org/10.20295/2412-9186-2026-12-02-128-137

Chicago

Leonid Baranov, Kulagin M. A., Yanchenko G. O.. "Selection of Artificial Intelligence Algorithms in Computer Vision Tasks for Detection and Localization of Objects on Railways." Transport automation research 12, no. 2 (2026): 128-137. https://doi.org/10.20295/2412-9186-2026-12-02-128-137.

WIKI

{{cite journal | author = Baranov L. A., Kulagin M. A., Yanchenko G. O. | title = Selection of Artificial Intelligence Algorithms in Computer Vision Tasks for Detection and Localization of Objects on Railways | journal = Transport automation research | year = 2026 | volume = 12 | issue = 2 | pages = 128-137 | doi = 10.20295/2412-9186-2026-12-02-128-137 }}

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SELECTION OF ARTIFICIAL INTELLIGENCE ALGORITHMS IN COMPUTER VISION TASKS FOR DETECTION AND LOCALIZATION OF OBJECTS ON RAILWAYS Selection of Artificial Intelligence Algorithms in Computer Vision Tasks for Detection and Localization of Objects on Railways

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