# 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 AP\({}_{75}\) of improved Mask R-CNN is 58.15%, the accuracy of PointNet is 92.4%, the average error of pole inclination is 0.66\({}^{\circ}\), and the variance is 0.12\({}^{\circ}\). It is proved that the method can effectively realize the automatic measurement of pole inclination. utility pole inclination angle; Mask R-CNN; PointNet; central axis fitting; 3D point cloud 202320203316882  
target, then extracts the centerline of the pole and calculates the inclination angle. [PERSON] et al. [9] used image processing methods such as Canny edge detector and Hough Transform to extract the linear features of the transmission tower, and then identified the target of the transmission tower. [PERSON] et al. [10] suggested the use of the backscattering coefficient to roughly separate the transmission tower from the background, obtain the most likely tower center point, and then calculate the inclination angle. [PERSON] et al. [11] proposed that the shadow contour of the transmission tower was extracted by K-means clustering and Hough Transform technique as the training dataset and the actual inclination angle was used as the label, and the pre-trained back propagation (BP) neural network was adopted to detect the inclination angle. Compared with traditional image processing algorithms, deep learning can extract features with stronger robustness, better generalization ability, and more accurate detection. [PERSON] et al. [12] suggested that the pole images be segmented by using the SegNet model, and then the segmented mask was filtered and morphologically processed; furthermore, Hough Transform was applied to get a line segment fitting the pole skeleton and calculate the angle. Finally, the maximum inclination angle from multiple perspectives was the detection result. [PERSON] et al. [13] applied YOLO-V4 to detect the transmission tower of interest in the region, the ResNet-50 model to detect the two endpoints of the transmission tower, and then the inclination angle was calculated through the endpoints. The authors [14; 15; 16] focused on using different deep learning models to identify and locate magnetic pole targets without calculation of the pole inclination. Angle detection based on a 3D point cloud is a normal approach. [PERSON] et al. [17] extracted the point cloud of the transmission tower from the point cloud data using unmanned aerial vehicle-light detecting and ranging (UAV-LiDAR). Based on the height distribution characteristics, it segmented the tower, located the central axis of the tower, and calculated the inclination angle of the tower. [PERSON] et al. [18] reconstructed the geometry of the transmission tower based on the 3D point cloud from LiDAR, and realized the inclination angle measurement of the transmission tower. [PERSON] et al. [19] extracted the central axis of the tower based on the contour line of the tower. The inclination of the tower was determined according to the reference direction of the ground. [PERSON] et al. [20] first obtained ungrounded point clouds, and then extracted the pole-like objects by independence analysis and circular or linear feature detection.