Defect Detection Method for Power Grid Lines Based on YOLOv8 Model and Domain-Specific Large Language Model

This paper presents a novel defect detection method for power grid lines using the YOLOv8 model and a domain-specific large language model. The YOLOv8 model is employed for its high detection efficiency and accuracy, enabling the identification of multiple types of defects in power grid line images. The domain-specific large language model, fine-tuned with a power grid dataset, generates professional descriptions, cause analyses, solutions, and preventive measures for the detected defects. Experimental results demonstrate that the YOLOv8 model outperforms YOLOv5 and YOLOv6, and the text results generated by the large language model show a similarity of over 70% with expert descriptions. This method significantly enhances the efficiency and professionalism of power grid line defect detection, reduces the cost of manual inspection, and improves the safety of power grid operations.