Pan-Sunet: Utility Corridor Understanding Using Spatial Layout Consistency

Abstract

Abstract. The article addresses the need for a dependable and efficient computer vision system to examine utility networks with minimal human intervention, given the deteriorating state of these networks. To classify the dense and irregular point clouds obtained from the airborne laser terrain mapping (ALTM) system, which is used for data collection, we suggest a deep learning network named Panoptic-Semantic Utility Network (Pan-SUNet). The proposed network incorporates three networks to achieve voxel-based semantic segmentation and 3D object detection of the point clouds at various resolutions, including object categories in three dimensions, and predicts two-dimensional regional labels to differentiate utility and corridor regions from non-corridor regions. The network also ensures spatial layout consistency in the prediction of the voxel-based 3D network using regional segmentation. By testing the proposed approach on 67 km2 of utility corridor data with an average density of 5 pts/m2, the paper demonstrates the effectiveness of the technique. The proposed network outperforms the state-of-the-art baseline network, achieving an F1 score of 94% for the pylon class, 99% for the ground class, 99% for the vegetation class, and 99% for the powerline class. It also shows high performance for 3D object detection for pylon and span achieving average precision of 99% and 92% respectively.