# Automatic Extraction of High-Voltage Power Transmission Objects from UAV Lidar Point Clouds
## 2 Related Work  
Research in transmission circumstance analysis involves both the classification of point clouds to extract transmission elements [16; 21; 22; 23; 24; 25; 26] and their reconstruction for safety analysis [27; 28; 29; 30; 31]. Methods and algorithms aiming at processing power transmission inspection data have bloomed in recent years,including both image and Lidar data acquired from various types of platforms, e.g., terrestrial laser scanning (TLS), ALS [32], and inspection robots [16]. The image-based methods mainly focus on the extraction of PLs. [PERSON] et al. [33] proposed a sequential local-to-global power line detection algorithm using morphological filtering and a graph-cut model to detect PLs. [PERSON] et al. [22] proposed a method based on statistical analysis and two-dimensional (2D) image processing to automatically extract PLs from ALS data. [PERSON] [34] presented a wavelet-based method to improve the extraction of PLs from low-cost imagery. However, image-based techniques can sometimes produce noisy results [35]. Instead of PL extraction using imagery data, methods of extracting 3D objects from point clouds have obtained more comprehensive spatial contents, e.g., coordinate, shape, and spatial distribution properties of power transmission elements [36; 37]. Many researchers have studied the extraction of power transmission objects from point cloud data. [PERSON] and [PERSON] [27] proposed a Hough transform (HT)-based automatic transmission conductor extraction method. Lidar points were separated by a rule-based classification considering HT. [PERSON] et al. [38] combined HT with a particle filter to detect PLs. [PERSON] and [PERSON] [39] proposed an automatic PL extraction method based on space-domain segmentation. [PERSON] and [PERSON] [40] used a voxel-based piece-wise line detector method and detected the PL orientation to identify the PL points. The above-mentioned methods extract the candidate PL points in the beginning, so the final detection quality is greatly influenced by the candidate PL extraction. [PERSON] and [PERSON] [41] developed a supervised classification using a random forest classifier. [PERSON] et al. [42] designed a classification method using the joint boost classifier. [PERSON] et al. [43] combined the joint boost method with some multiscale features to classify HV-bundled conductors. These knowledge-based classifications were sensitive to the scene diversity and the success rate was affected by factors such as data gaps, despite the high classification rate. [PERSON] and [PERSON] [25] extracted transmission lines through the connectivity analysis of 3D hierarchical voxels. [PERSON] et al. [2] segmented the PL points based on dimensional features, utilizing a height histogram to obtain a relatively accurate separation interface. However, most of these methods require the filtering terrain points in the preprocessing stage, so they can be limited by terrain-filtering algorithms. On the other hand, a range of results have been presented for the detection and modeling of pylons. [PERSON] et al. [44] employed monocular depth estimation to recognize and reconstruct pylons. [PERSON] et al. [45] utilized serialized images to detect the locations of pylons. In this method, non-electric-transmission objects were separated by a global threshold, which cannot be easily applied to data covering large scenes with topographic fluctuations or with a wide range of mountainous areas in complex natural environments. [PERSON] et al. [31] proposed a detection method that provided individual pylon locations by Markov random field. [PERSON] et al. [46] and [PERSON] et al. [47] proposed unsupervised extraction methods based on 2D-mask and linear rectification of PLs and pylons. However, tall, highly dense trees in the environment affected the recognition of power towers. [PERSON] et al. [48] and [PERSON] et al. [49] constructed pylons from a library of 3D parametric models using polyhedrons based on stochastic geometry. [PERSON] et al.