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SPR-DETR: DETR with Self-Supervised Learning and Position Relation Modeling for UAV-Based Catenary Support Component

Tao Liang¹, Zhigang Liu¹, Linjun Shi¹

¹School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610097, China.

Sensors (Basel, Switzerland)

|May 27, 2026

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This study introduces a novel framework for detecting railway Catenary Support Components (CSCs) using self-supervised learning and advanced vision modules. The method improves detection accuracy and reduces annotation costs for railway infrastructure inspection.

Area of Science:

Railway Engineering
Computer Vision
Artificial Intelligence

Background:

Catenary Support Components (CSCs) are critical for electrified railways, but their detection in images is challenging due to limited labeled data, complex backgrounds, and scale variations.
Existing detection methods struggle with the scarcity of annotated data and the diverse visual conditions encountered in real-world railway environments.

Purpose of the Study:

To develop a robust and efficient framework for detecting Catenary Support Components (CSCs) in railway images.
To address the challenges of limited labeled data, complex backgrounds, and multi-scale variations in CSC detection.
To reduce the cost and effort associated with data annotation for railway infrastructure monitoring.

Main Methods:

A Siamese-based self-supervised learning framework was employed for pre-training to leverage unlabeled data and minimize annotation requirements.

Keywords:

catenary support components (CSCs)deep learning object detection self-supervised learning

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End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

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Published on: December 15, 2023

Novel modules, Vision Attention-based Intrascale Feature Interaction (Vision-AIFI) and Relation Vision Module (RVM), were introduced to enhance multi-scale feature extraction and handle background complexities.

A Dempster-Shafer (DS) evidence theory-based detection head was integrated to improve classification confidence and localization precision.

Main Results:

The proposed framework achieved high detection performance, with mAP of 77.84, APs of 67.84, APm of 70.31, and APl of 90.04 on a newly constructed UAV-based CSC dataset.
The self-supervised pre-training significantly reduced the need for labeled data, lowering annotation costs.
Domain generalization experiments demonstrated the framework's strong adaptability and high detection accuracy in real-world scenarios.

Conclusions:

The developed framework effectively addresses the challenges in Catenary Support Component detection, offering a cost-effective and accurate solution.
The integration of self-supervised learning, advanced feature interaction modules, and DS evidence theory provides a powerful tool for automated railway infrastructure inspection.
The study highlights the potential of deep learning approaches for enhancing the safety and efficiency of railway operations through improved component monitoring.