Segmentation of the communication tower and its accessory equipment based on geometrical shape context from 3D point cloud

Abstract

Communication tower (CT) and its accessory equipment (AE) such as radio frequency equipment (RFE) and antenna, are essential in providing highspeed and stable mobile network services. It is necessary to routinely monitor the security and stability of CT and AE for seamless communication. There is limited research on fine segmentation of communication base station objects. This paper proposes a method for accurately segmenting the point cloud of the CT and AE from Terrestrial Laser Scanning (TLS) data. At first, the CT point cloud is accurately segmented based on region growing and Random Sample Consensus (RANSAC). Then, the point cloud of pole-shaped apart is extended to a certain distance to obtain the buffer point cloud containing AE. Normal Differential (ND) clustering is employed to obtain several groups of clusters containing planes, and calculate each plane's filling rate and size. Finally, the cluster type (such as antenna, RFE, or other) is distinguished. The experimental results demonstrate that the point-based average F1-score of CTs is 98. 70%, the point-based and object-based average F1-scores of antennas are 96. 09% and 97. 93%, and the corresponding values for the RFE are 89. 89% and 90. 00%, respectively, indicating the optimal performance of the proposed method.