Automatic Object Extraction from Electrical Substation Point Clouds
AbstractThe reliability of power delivery can be profoundly improved by preventing wildlife-related power outages. This can be achieved by insulating electrical substation components with non-conductive covers. The manufacture of custom-built covers requires as-built models of the salient components. This study presents new, automated methodology to recognize key components of electrical substations from 3D LiDAR data acquired using terrestrial laser scanning. The proposed methodology includes six novel algorithms to recognize key components (fence, cables, circuit breakers, bushings and bus pipes) of electrical substations. Three datasets with different resolutions and configurations are used in this study. A Leica HDS 6100 laser scanner was used to acquire the first dataset and a Faro Focus3D laser scanner was employed to collect the second and third datasets. The obtained results indicate that 178 and 171 out of 181 electrical substation elements were successfully recognized in the first and second dataset, respectively, and 183 out of 191 components were identified in the third dataset. The results also demonstrate that an average 97.8% accuracy and average 98.8% precision at the point cloud level can be achieved. View Full-Text
Share & Cite This Article
Arastounia, M.; Lichti, D.D. Automatic Object Extraction from Electrical Substation Point Clouds. Remote Sens. 2015, 7, 15605-15629.
Arastounia M, Lichti DD. Automatic Object Extraction from Electrical Substation Point Clouds. Remote Sensing. 2015; 7(11):15605-15629.Chicago/Turabian Style
Arastounia, Mostafa; Lichti, Derek D. 2015. "Automatic Object Extraction from Electrical Substation Point Clouds." Remote Sens. 7, no. 11: 15605-15629.