Insulator Recognition Based on Moments Invariant Features and Cascade AdaBoost Classifier

A method based on moments invariant features and cascade AdaBoost classifier for insulator recognition is put forward to solve the problem of poor performance of insulator recognition. At first, the insulator image is preprocessed by median filtering, dilating, eroding and Otsu thresholding. Then, for the better extraction of moments invariant features, the preprocessed insulator image is tilted correctly based on PCA (Principal Component Analysis). Next, the moments invariant features are extracted and chosen to compose complex classifier in the process of training AdaBoost. Finally, the complex AdaBoost classifiers are combined in a cascade method for insulator recognition. The results of experiments demonstrate that the proposed method can recognize the insulator from complex background in the mountainous area, and it has better robustness, accuracy and validity.