Intelligent Inspection System for Power Insulators Based on AAV on Complex Weather Conditions

This paper presents an intelligent inspection system for power insulators, integrating automous aerial vehicles (AAV) to address complex conditions. A novel image enhancement technique, based on the multi-scale Retinex algorithm with combined illumination correction and compensation, is proposed to enhance both the overall image quality of insulators and the visual perception for human observers. The study employs the state-of-the-art YOLOv7 target detector for automated identification of insulator defects. The YOLOv7 model undergoes further optimization, enhancing the feature extraction structure, incorporating the SE attention mechanism, and refining the loss function. These modifications enable the model to adapt to more intricate intelligent inspection tasks related to transmission line insulators, resulting in improved efficiency and detection accuracy. Building on this research out-comes, our team introduces an intelligent power insulator detection software system tailored for AAV inspection scenarios.