A Review and Prospect of Appearance Defect Detection Methods for High Voltage Equipment Based on Vision Large Models

Abstract

High voltage equipment, as a core component of power systems, plays an indispensable role in ensuring the reliability of power supply through its safe and stable operation. Traditional visual defect detection for high voltage equipment, however, often relies on manual inspection and experience‐based judgement, which struggles to meet the growing monitoring demands. Consequently, the introduction of more intelligent and precise large‐model technologies into the domain of high voltage equipment visual defect detection information processing is urgently required. The large‐scale foundational visual models, with their robust data processing capabilities, complex pattern recognition and reasoning abilities, are progressively penetrating various industries, serving as a key driver for industrial upgrades and transformation. In the field of high voltage equipment, the application prospects of foundational visual models are particularly promising. In this paper, we provide a comprehensive review of the research progress on visual large models in the specific context of high voltage equipment. It summarises a concise overview of the engineering challenges that foundational visual models need to tackle. Furthermore, based on research achievements from the era of smaller models, we thoroughly examine the application potential of large‐model technologies in the high voltage equipment domain. Additionally, we also identify and scrutinise the scientific issues associated with visual large models. At present, research on vision foundation models for high voltage equipment is still in its early stages both domestically and internationally. Significant gaps remain in areas such as dataset construction, pretraining and fine‐tuning, all of which present promising directions with immense application potential. The primary objective of this paper is to offer a comprehensive and systematic review of large pretrained models for high voltage equipment defect detection methods, providing valuable reference for researchers exploring this field.