A Lightweight Model for Insulator Defect Detection Based on Vision–Language Modeling and Prior Knowledge in Power Systems

Insulators serve as critical insulating components in power transmission lines, and their defects are one of the primary causes of power outages in power grids. Power companies widely utilize unmanned aerial vehicle (UAV) inspections to collect image data of power transmission lines. However, existing methods face two core challenges: at the data level, insulator defect samples are extremely scarce in massive image datasets, leading to severe data imbalance issues. At the algorithm level, deep learning-based defect detection methods rely on data-driven feature extraction, ignoring quantifiable prior knowledge such as insulator installation specifications and mechanical structure. This factor results in low localization efficiency and poor robustness in complex scenarios. To address these issues, this paper proposes an insulator defect detection method based on Vision–Language models and prior knowledge. It extracts prior knowledge about the physical characteristics of insulators, quantifies spatial structure and installation specifications as prior constraints, embeds prior knowledge into the vision–language model’s feature space to generate insulator defect samples, addresses the data imbalance issue, and detects insulator defects using an improved You Only Look Once (YOLO) algorithm. This approach reduces model parameters while maintaining detection accuracy, constructing a lightweight model for insulator defect detection. The experimental results show that, compared with PP-YOLOE-m and RT-DETR-R18 models, the method proposed in this paper can significantly improve the detection accuracy. The mean average precision indicator of the model in this paper has reached 95.7%.