Research On Transmission Line Insulator Defect Detection Method Based on Yolov8
DOI:
https://doi.org/10.62051/ijcsit.v3n2.29Keywords:
Transmission line, Insulator, Defect detection, YOLOv8, Deep LearningAbstract
Transmission line insulators are an important part of the power system, and their defects can seriously affect the safety and reliability of the power system. The traditional insulator defect detection method mainly relies on manual inspection, which is inefficient, costly, and easily affected by subjective factors. In order to improve the efficiency and accuracy of insulator defect detection, this paper proposes a YOLOv8-based insulator defect detection method for transmission lines. The method makes use of the powerful target detection capability of the YOLOv8 model to identify and locate transmission line insulator images and classify defect types. The experimental results show that the method achieves good results in insulator defect detection, can effectively identify and locate different types of insulator defects, and provides a reliable technical guarantee for the safe operation of transmission lines.
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