Precision in Aerial Surveillance: Integrating YOLOv8 With PConv and CoT for Accurate Insulator Defect Detection

Precision in Aerial Surveillance: Integrating YOLOv8 With PConv and CoT for Accurate Insulator Defect Detection

Blog Article

Insulator defect detection using autonomous 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, color touch 7/97 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 camo iphone se case solution for automated, precise power line inspections.