# Automatic Measurement of Inclination Angle of Utility Poles Using 2D Image and 3D Point Cloud
###### Abstract
Finally, the objects were classified as street lights, utility poles, and traffic sign poles through 3D shape matching. [PERSON] et al. [21] put forward a shape-based segmentation model and region generation algorithm to detect pole-like objects in voxelized point clouds, and then classified pole-like objects according to shape and height features. [PERSON] et al. [22] detected pole-like objects from coarse to thin. For the preprocessed point cloud, the pole-like objects on the roadside were gradually segmented through a variety of different segmentation methods. [PERSON] et al. [23] removed the ground point cloud, and then used the horizontal cross section analysis and the minimum vertical height standard to extract the pole-like objects from each clustered point cloud. Finally, the pole-like objects were classified according to height and geometry. The image segmentation algorithms based on deep learning methods include FCN, U-Net, SegNet, DeepLab series, Mask R-CNN, YOLACT, SOLO, etc. [24; 25]. FCN, U-Net, Segnet, and the DeepLab series belong to semantic segmentation. FCN, with full convolution layer, is the first model to use deep learning to realize image semantic segmentation, where the end-to-end network is constructed by convolution and deconvolution to realize the classification of each pixel. U-Net adopts the U-shaped network structure for encoding and decoding based on FCN, and uses skip connections to effectively fuse the encoded shallow information and the decoded deep information. SegNet is similar to U-Net, but the biggest difference is the use of down sampling and maximum pooling index for up sampling instead of deconvolution during decoding, which reduces the training parameters. The DeepLab series obtains multi-scale information through technologies such as atrous convolution and atrous spatial pyramid pooling, and has high invariance to spatial transformation. Mask R-CNN, YOLACT, and SOLO belong to instance segmentation, which can complete segmentation and target detection tasks at the same time. YOLACT splits the instance segmentation task into two parallel subtasks, one of which uses FCN to generate prototype masks, another of which generates detection boxes and mask coefficients. SOLO series algorithms are not affected by the location of the anchor, and instance category is introduced to transform the instance mask segmentation problem into a classification problem. Mask R-CNN is improved from Faster R-CNN. The segmentation task is added in parallel to the original classification and regression tasks, which realizes the high-precision instance segmentation effect with a small computational cost. Compared with the above image segmentation algorithms, instance segmentation has the function of target detection, which can not only realize the positioning of the poles, but also is helpful to expand the inspection content of the poles. Compared to the single-stage segmentation models, Mask R-CNN [26] is a two-stage detection model that predicts masks in bounding boxes with higher segmentation accuracy. Mask R-CNN is a flexible framework in which different branches are easily added to complete different tasks, so it is more suitable for complex street scene environments. Point cloud segmentation algorithms based on deep learning usually include MVCNN, VoxNet, PointNet, PointNet ++, RSNet and DGCNN, etc. [27]. MVCNN is a model based on multi-view, which can be applied to extract features from projected images of point clouds from different perspectives; however, the multi-view method will lose geometric spatial information, resulting in inaccurate segmentation. VoxNet is a typical point cloud voxelization method, which is convenient for feature extraction using neural networks. However, there are also problems such as low efficiency of voxel grid arrangement due to point cloud sparsity, large memory occupation, and information loss.