Insulator defect detection using autonomous vegetable glycerin for sale aerial vehicles (AAVs) images is a promising method for power transmission line inspections.However, varying sizes, orientations, and complex backgrounds of insulator defects result in high false negatives and low accuracy.Previous studies have not adequately incorporated self-attention mechanisms focusing on adjacent keys.To address this, we propose an improved YOLOv8-based detection algorithm.We added a Contextual Transformer module to the YOLOv8 backbone for better contextual understanding and introduced a Partial Convolution layer to reduce redundant calculations.
Our model shows improvements over existing ones, achieving a precision of 97.5%, a mean average precision of 86.2%, and a recall of 81.1%, offering a robust solution for automated, ivoryjinelle.com precise power line inspections.