The routine power line inspection is critical to maintain the reliability, availability, and sustainability of electricity supply. As a key part of inspection, power lines and pylons extraction is essential for resource management and power corridor safety, especially in the mountain regions. In this paper, we proposed a deep learning based method to extract power lines and pylons using ALS point clouds. First, a structure information preserved module is designed to mine the relationship of local neighborhood points. Then, a graph convolutional network (GCN) is used as basic module to extract point features. Finally, three categories, power lines, pylons and other objects are segmented from input point clouds. In addition, we provide an effective data enhancement strategy to generate enough samples to train the proposed model. We evaluated our method using a dataset acquired by our ALS scanning system. Experimental results demonstrate that our method is superior to the state-of-the-art methods on descriptiveness and efficiency. The overall accuracy and mean time are 99.1% and 9.3 seconds, respectively.
|Published - 17 Feb 2021
|IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium - Waikoloa, HI, USA
Duration: 26 Sept 2020 → 2 Oct 2020
|IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium
|26/09/20 → 2/10/20
- Power line，pylon extraction，ALS，point cloud，graph convolutional network