[PERSON] et al. [30] consider that the insulators in aerial images often exhibit the problem of texture inhomogeneities. To solve this problem, the texture features of insulators is extracted by a semi-local operator under the Beltrami framework. Then, a new active contour is proposed to extract insulators from an aerial image. However, this method is time-consuming and far from a practical application. In the work of [22; 24; 25], the key-point features of the insulator are analyzed. Subsequently, the SURF or DoG (Difference of Gaussians) key-point features are used to locate the insulators in an aerial image. Finally, an elliptical spatial descriptor is applied to check the insulator faults. However, although these methods can achieve perfect performance in some cases, they can be easily affected by the filming angle and distance when applied in practical applications. To enhance the robustness of insulator faults detection. [PERSON] et al. [27] adopt multiple insulator features containing color, shape, and texture, to extract the insulators from the complex background. After that, they develop an insulator piece-to-piece distance-based strategy to detect the insulator fault. However, due to the high computational complexity of the feature extraction strategy, this method is also far from a real-time application. Moreover, this method is only applicable to independent situations among adjacent insulators in the aerial image. Based on observations of many man-made feature-based methods, these methods are quite sensitive to background interference. Moreover, their performance is usually suppressed by the filming angles and filming distances. In the machine learning-based methods, AdaBoost [33; 34], Sparse representation-based classifier [35], SVM [36], Cascade classifier [37], and KNN [38] are applied to locate the insulator positions and detect the insulator faults. [PERSON] and [PERSON] [34] extract seven invariant moment features of insulator by using samples from 300 insulator aerial images to train an AdaBoost classifier. Then, they use the trained AdaBoost classifier to locate the insulators in the aerial image. Finally, they design a strategy similar to that of [27] to detect insulator faults. Experiment results show that their proposed method can be used to detect multi-fault in an insulator string. However, because the filming angle and distance are always changing during UAV inspection, the insulators in aerial images are usually overlapped, which means it is quite hard to obtain the spatial information of each insulator. Consequently, the method proposed in [34] can only achieve good performance when insulators are isolated. In [35], an insulator dataset is constructed and the HOG (Histogram of Oriented Gradient) feature of each image is calculated. After that, the PCA (Principal Component Analysis) is applied to create an over-complete dictionary for each insulator sample. Finally, a sparse-representation-based classifier is trained to obtain the positions of insulator faults. However, only using an over-complete dictionary of the HOG feature to train a classifier cannot achieve good performance in the background with complex texture interference. To compensate for this shortcoming. [PERSON] et al. [36] applied fusion features composed of HOG and LBP (Local Binary Pattern) features to train an SVM (Support Vector Machine) classifier. Experiment results show that this method can obtain multi-angle insulator's locations in complex scenes. In the work of [37], haar-like feature, integral graph feature and directional gradient histogram feature are combined to train a cascade classifier and an SVM classifier. Then, the two classifier models are applied to locate the insulators. Finally, the fault location can be determined by an incremental contour value-based strategy. In [38], a KNN classifier is trained to distinguish between insulator caps and background clutter. Then, an automatic insulator fault detector is developed to analyze each cap for faults based on an elliptical descriptor.