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.

The monitoring of telegraph poles as essential features supporting overhead distribution network lines is the primary subject of this work. This paper proposes a method for locating and extracting telegraph poles from an image matching-based point cloud. Firstly, the point cloud of the poles is extracted using the planar grid segmentation clustering algorithm and the connected component analysis algorithm of the region grows according to the isolated features of the poles perpendicular to the ground. Secondly, the candidate telegraph poles are located based on the suspension point of the buffer, considering that the top of the pole is connected to the power suspension line. Thirdly, the horizontal projection method of the backbone area is utilized to eliminate the interference of vegetation in the buffer area. Finally, the point cloud of the telegraph pole is extracted through the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The experimental results demonstrate that the average values of Recall, Precision, and F1-score in telegraph pole detection can reach 91.09%, 90.82%, and 90.90%, respectively. The average RMSE value of location deviation is 0.51m. The average value of the F1-score in the telegraph pole extraction is 91.83%, and the average extraction time of a single pole is 0.27s. Accordingly, this method has strong adaptability to areas with lush vegetation and can automatically locate and extract the telegraph pole point cloud with high accuracy, and it can still achieve very high accuracy even under the holes in the data.

The settlement and tilt of a communication base station’s cabinet can harm electronic components, affecting stable operation. A 3D model measures cabinet deformation using LiDAR data, facilitating timely maintenance. While limited studies address cabinet segmentation, existing methods, relying on line or plane detection, face challenges with interference from objects sharing features with the cabinet. This paper proposes a cabinet point cloud segmentation method that combines cluster analysis and plane Point Cloud Coverage (PCC). The method preprocesses the point cloud, analyzes the differences in normal vector characteristics in different points neighborhoods, segments the point cloud based on the given normal vector value, performs Euclidean clustering, and extracts the planar point cloud using the RANSAC algorithm to judge if it meets the characteristics of the cabinet. The proposed method is validated with nine datasets, demonstrating a 98.23% average F1-score. Results confirm the algorithm’s accuracy in extracting cabinet point cloud, showcasing its versatility compared to common algorithms.