Location and Extraction of Telegraph Poles from Image Matching-Based Point Clouds

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 3; p. 433
• Main Authors: Wang, Jingru, Wang, Cheng, Xi, Xiaohuan, Wang, Pu, Du, Meng, Nie, Sheng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2022
• Subjects: Accuracy; Adaptability; Algorithms; Buffers; Clustering; connected component analysis; conservation buffers; data collection; Data integrity; Datasets; Electricity distribution; extracting; grid segmentation; Image segmentation; locating; Matching; Methods; Noise; Poles; Remote sensing; telegraph poles; the distribution network line inspection; Unmanned aerial vehicles; Vegetation
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14030433

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.