PriKMet: Prior-Guided Pointer Meter Reading for Automated Substation Inspections

Despite the rapid advancement of smart-grid technologies, automated pointer meter reading in power substations remains a persistent challenge due to complex electromagnetic interference and dynamic field conditions. Traditional computer vision methods, typically designed for ideal imaging environments, exhibit limited robustness against real-world perturbations such as illumination fluctuations, partial occlusions, and motion artifacts. To address this gap, we propose PriKMet (Prior-Guided Pointer Meter Reader), a novel meter reading algorithm that integrates deep learning with domain-specific priors through three key contributions: (1) a unified hierarchical framework for joint meter detection and keypoint localization, (2) an intelligent meter reading method that fuses the predefined inspection route information with perception results, and (3) an adaptive offset correction mechanism for UAV-based inspections. Extensive experiments on a comprehensive dataset of 3237 substation meter images demonstrate the superior performance of PriKMet, achieving state-of-the-art meter detection results of 99.4% AP50 and 85.5% for meter reading accuracy. The real-time processing capability of the method offers a practical solution for modernizing power infrastructure monitoring. This approach effectively reduces reliance on manual inspections in complex operational environments while enhancing the intelligence of power maintenance operations.