Extracting masts of overhead supply and street lights from point cloud

Abstract: Regular inspection and documentation for railway assets are necessary to monitor the status of the traffic environment. Mobile Laser Scanning (MLS) makes it possible to collect highly accurate spatial information of railway environments in the form of point cloud, and an automatic method to extract interested objects from the point cloud is needed to avoid too much manual work. In this project, point cloud along a railway in Saltsjöbanan was collected by MLS and processed to extract interested objects from it. The main purpose of the project is to develop a workflow for automatic extraction of masts of overhead supply and street lights from the study area. Researchers have proposed various methods for object extraction, such as model-based method, shape-based method, semantic method, and machine learning method recently. Different methods were reviewed and Support Vector Machine was chosen for the classification. Several softwares were reviewed as well. TerraScan and CloudCompare were chosen for pre-processing, and the major part was done in MATLAB. The proposed method consists of 4 steps: pre-processing, voxelization and segmentation, feature computation, classification and validation. The method calculates features to describe every object segmented from the point cloud and learns from the manually classified objects to train a classifier. The study area was divided into training data and validating data. The SVM classifier was trained using training data and evaluated using validating data. In the classification, 90.84% of the masts and 67.65% of the lights were correctly classified. There was some object loss during the step of pre-processing and segmentation. When including the loss from the pre-processing and segmentation step, 87.5% of the masts and 53.49% of the lights were successfully detected. The street lights have more various outlook and more complicated surrounding environment, which caused a relatively low accuracy.