Wan NiuThis email address is being protected from spambots. You need JavaScript enabled to view it.
School of Electronics and Electrical Engineering, Zhengzhou University of Science and Technology, Zhengzhou 450064 China
Received: January 24, 2026 Accepted: March 5, 2026 Publication Date: March 27, 2026
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
Overhead conductors are critical components of power transmission systems, and their corrosion-induced degradation poses severe threats to grid stability and operational safety. Traditional corrosion inspection methods rely on manual patrols or single-source data analysis, suffering from inefficiency, subjectivity, and limited accuracy in dynamic environments. To address these limitations, this paper proposes a real-time corrosion estimation framework for overhead conductors by fusing UAV-acquired RGB images and load current data. First, an improved YOLOv8 model integrated with a Coordinate Attention (CA) module is designed to segment corroded regions from RGB images, enabling quantitative extraction of corrosion features (e.g., rust layer fractal dimension, crack density). Second, a physics-informed corrosion rate model is established to characterize the synergistic effect of load current and environmental factors, based on Faraday’s electrolysis law and corrosion electrochemistry theory. Third, a Bayesian network-based data fusion strategy is developed to integrate image-derived features and current-based corrosion rates, realizing accurate estimation of corrosion severity (intact, mild, moderate, severe, failure risk). Experiments are conducted on a 110kV transmission line segment in an industrial polluted area, where 21026 UAV RGB images and 3 months of load current data are collected. The results demonstrate that the proposed framework achieves a corrosion severity classification accuracy of 92.3% outperforming single-source methods and state-of-the-art fusion models. The inference speed reaches 28FPS, satisfying real-time inspection requirements. This study provides a reliable technical solution for intelligent corrosion monitoring of overhead conductors, supporting data-driven power grid maintenance decisions.
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