A Novel Framework for Distribution Power Lines Detection

Millions of dollars are spent yearly to trim trees along rights-of-way and guarantee reliable distribution line systems. To reduce these costs, power utilities are embracing a new approach based on light detection and ranging (LiDAR) data. They aim to automatically detect the locations of critical b...

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Bibliographic Details
Main Authors: Abongo, Damos Ayobo, Gaha, Mohamed, Cherif, Safa, Jaafar, Wael, Houle, Guillaume, Buteau, Christian
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
Buildings
distribution line detection
Laser radar
Machine learning
Mobile LiDAR
Point cloud compression
post-processing
power lines
Roads
Vegetation
XGBoost
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Summary:Millions of dollars are spent yearly to trim trees along rights-of-way and guarantee reliable distribution line systems. To reduce these costs, power utilities are embracing a new approach based on light detection and ranging (LiDAR) data. They aim to automatically detect the locations of critical branches/trees and assess their risks. In this paper, we propose a novel and robust power lines detection framework with several LiDAR data processing steps, which combines machine learning and geometric approaches. By combining these methods, we efficiently detect distribution lines with an Intersection-over-Union performance superior to those of deep-learning-based benchmarks, and less complex than most of them. The benefit is that by prescribing the use of geometrical/mathematical approaches for the post-processing of deep-learning/machine-learning outputs, we are able to further improve lines detection. Finally, we expect our novel framework to be generalized to detect various LiDAR objects such as poles, cars, buildings and roads.
ISSN:2642-7389
DOI:10.1109/ISCC58397.2023.10217856