Abstract: The transmission line plays an important part in power transmission task, so it is of great significance to identify its defects for the maintenance, and the severe power accidents can be avoided or decreased. For the background of images captured by unmanned aerial vehicle is very complex and difficult to be detected, a radial basis probabilistic neural network based fault location identification method for transmission lines is proposed. Firstly, the weighted color difference method, maximum interclass variance method and morphological filtering are sequentially adopted to realize the accurate segmentation of transmission lines in complicated background. Secondly, the segmented line area is equally divided into 10 line sub-images, 40 texture enhancement sub-images at 8 angles and 5 dimensions of transmission lines are obtained by Gabor filter, and the roughness,contrast and orientation of each sub-image are also extracted. By the feature variance, 10 strong texture features are selected and adopted as the input parameters to the radial basis probabilistic neural network for the defect identification of transmission line. The results show that both the rapid segmentation of transmission lines and the accurate identification of the defects based on the images in the complex background can be achieved by the proposed method, which provides a new idea for the operation state detection of transmission line in unmanned aerial vehicle inspection.