Fast 3D Transmission Tower Detection Based on Virtual Views - Liwei Zhou; Jiaying Tan; Jing Fu; Guiwei Shao (Applied Sciences)
Advanced remote sensing technologies leverage extensive synthetic aperture radar (SAR) satellite data and high-resolution airborne light detection and ranging (LiDAR) data to swiftly capture comprehensive 3D information about electrical grid assets and their surrounding environments. This facilitates in-depth scene analysis for target detection and classification, allowing for the early recognition of potential hazards near transmission towers (TTs). These innovations present a groundbreaking strategy for the automated inspection of electrical grid assets. However, traditional 3D target detection techniques, which involve searching the entire 3D space, are marred by low accuracy and high computational demands. Although deep learning-based 3D target detection methods have significantly improved detection precision, they rely on a large volume of 3D target samples for training and are sensitive to point cloud data density. Moreover, these methods demonstrate low detection efficiency, constraining their application in the automated monitoring of electricity networks. This paper proposes a fast 3D target detection method using virtual views to overcome these challenges related to detection accuracy and efficiency. The method first utilizes cutting-edge 2D splatting technology to project 3D point clouds with diverse densities from a specific viewpoint, generating a 2D virtual image. Then, a novel local–global dual-path feature fusion network based on YOLO is applied to detect TTs on the virtual image, ensuring efficient and accurate identification of their positions and types. Finally, by leveraging the projection transformation between the virtual image and the 3D point cloud, combined with a 3D region growing algorithm, the 3D points belonging to the TTs are extracted from the whole 3D point cloud. The effectiveness of the proposed method in terms of target detection rate and efficiency is validated through experiments on synthetic datasets and outdoor LiDAR point clouds.