This paper presents a detection method of insulator stings for aerial inspection based on feature-fusion. The local subimages of insulator strings are firstly collected from aerial videos and tagged to establish a training dataset. The fusion feature is then composed by the histogram of oriented gradients (HOG) feature and local binary pattern (LBP) feature after the principal component analysis (PCA) dimension reduction separately. A training model is developed by SVM This paper presents a detection method for estimation of vanishing point position with designed regression

convolutional neural network. Due to the deep structures of convolutional networks, global high-level features are

extracted from the whole image, which helps to locate the vanishing point. In this paper, we provide a new structure of

regression neural network based on AlexNet. The structure consists of five convolutional layers, four fully connected

layers, Tanh activation function and regression loss function. We feed the neural net with a small number of training

dataset and the result proves that this method is adaptable. Compare to classical method, deep learning is more effective

on blurred pictures and complex circumstances.