# Automatic Measurement of Inclination Angle of Utility Poles Using 2D Image and 3D Point Cloud
###### Abstract
PointNet is a point cloud segmentation method directly based on point cloud, which directly takes a point cloud as input, uses spatial transformation network to solve the problem of rotation invariance, and uses symmetric function to fuse point cloud information to achieve global feature extraction. PointNet reduces the computational complexity with higher classification and segmentation accuracy. However, it also has the defects of weak extraction of local information and loss of details in complex scenes. PointNet++ adds a local area division module based on PointNet to fuse local features to improve the segmentation effect. RSNet converts the unordered point cloud into an ordered sequence by slicing the pooling layer, then processes the sequence by using the RNN layer, updates the features, and finally, maps back to each point by slicing the pooling layer. DGCNN combines graph convolution neural network (GCNN) with PointNet, replacing the multi-layer perception (MLP) network in PointNet with edge convolution, and also achieves significant results. The above segmentation models have achieved good segmentation results in the indoor environment, but the outdoor environment is more abundant and complex, and the point cloud segmentation is still difficult. In all the studies reviewed here, these studies have together provided important insights into the measurement of the pole inclination angle, but still have some limitations, detailed as follows. An effective and automatic pole inclination angle detection method is essential and necessary for a power system [1; 2; 3; 4; 5; 6]; however, there is still no such measurement method at present. The traditional instrument measurement method [7; 8] is suitable for manually measuring whether the pole installation meets the requirements, and not for automatic inspection. 1. The 2D image measurement method [9; 10; 11; 12; 13; 14; 15; 16] can only measure the pole inclination angle in one direction at a time, owing to the lack of depth information; therefore, the pole inclination angle can be estimated by measuring several times. 2. Without large labeled reference pole data, locating the pole in the 3D point cloud is time consuming; additionally, the resolution of the 3D point cloud is relatively low, and thus, the pole segmentation result from the point cloud in a complex background with a relatively large search space is usually not ideal [17; 18; 19; 20; 21; 22; 23]. Aiming to address these challenges, we put forward a novel method using both 2D image and 3D point cloud to realize the automatic measurement of the inclination angle of poles at once. The main contributions of this paper are as follows:  
1. The accuracy of pole skeleton segmentation is improved by expanding the bounding box of Mask R-CNN to different values and adding attention mechanism to the head network of Mask R-CNN. 2. The method of piecewise fitting is used to realize the fitting of the central axis of cylinder-like objects, such as utility poles, and calculate the inclination angle, which increases the accuracy of angle calculation in the case of interference points. 3. The fusion of 2D image and 3D point cloud makes full use of their complementary features, which can not only realize the measurement of pole inclination at once, but also meet the requirements of automatic inspection of poles. The rest of the paper is organized as follows. Section 2 explains the proposed utility pole inclination angle detection approach, followed by experiments and performance evaluation in Section 3. Finally, conclusion and future works are given in Section 4.