Automatic Measurement of Inclination Angle of Utility Poles Using 2D Image and 3D Point Cloud

Abstract

The utility pole inclination angle is an important parameter for determining pole health conditions. Without depth information, the angle cannot be estimated from a 2D image, and without large labeled reference pole data, it is time consuming to locate the pole in the 3D point cloud. Therefore, this paper proposes a method that processes the pole data from the 2D image and 3D point cloud to automatically measure the pole inclination angle. Firstly, the mask of the pole skeleton is obtained from an improved Mask R-CNN. Secondly, the pole point cloud is extracted from a PointNet that deals with the generated frustum from the pole skeleton mask and depth map fusion. Finally, the angle is calculated by fitting the central axis of the pole cloud data. ApolloSpace open dataset and laboratory data are used for evaluation. The experimental results show that the AP75 of improved Mask R-CNN is 58.15%, the accuracy of PointNet is 92.4%, the average error of pole inclination is 0.66°, and the variance is 0.12°. It is proved that the method can effectively realize the automatic measurement of pole inclination.