Hydrophobicity Level Detection of Composite Insulators Based on Random Forest
Aiming at the issues that the traditional composite insulator hydrophobicity detection is time-consuming and labor-intensive,and there are large errors of detection results affected by the environment and other factors,the author proposes a composite insulator hydrophobicity level detection method based on Random Forest.This method is a machine learning algorithm,through building a classification model by constructing multiple decision trees and voting,preprocesses the surface of the composite insulator umbrella skirt using image processing methods such as enhancement,smoothing,denoising,and segmentation,to extract hydrophobic related feature data,and finally completes the detection of composite insulators hydrophobic level by using the Random Forest.During the process of detecting and analyzing,when the number of decision trees is around 100,the accuracy rate is stable at 92.88%,which improves the efficiency and accuracy of hydrophobicity detection comparing with the traditional composit insulator hydrophobicity detecting method.
composite insulatorshydrophobicityrandom forestimage processingdecision Tree
万方数据
维普
