# Fast 3D Transmission Tower Detection Based on Virtual Views
## 5 Conclusions  
With the rapid development of new types of power systems, the ability to detect 3D TTs during the use of drones for automated power grid inspection is a core technical element for understanding and mining potential environmental hazards. However, traditional 3D TT detection methods require searching the entire three-dimensional space, which is not only inaccurate but also very time-consuming. In recent years, although 3D target detection methods based on deep learning have significantly improved detection accuracy, they rely on a large number of 3D TT samples for training, are sensitive to the density of 3D point clouds, and have low detection efficiency, limiting their application in automatic inspection of power transmission corridors.  
This paper, based on the projected virtual view, converts irregular three-dimensional point clouds into regular 2D virtual views for detection, which not only effectively utilizes mature image processing technology, especially the proposed local-global dual-path feature fusion network, to achieve efficient feature extraction, but also promotes the integration of multimodal information, combining the depth information of point clouds with the rich texture of images, greatly improving detection accuracy in complex scenes. The overall average accuracy of 3D detection of TTs can reach 83%, and the detected point cloud resolution can reach 0.2 m, which can meet the needs of drone inspection of power transmission corridors.  
However, at the same time, the proposed method also has some shortcomings, such as the extracted TT point cloud is not pure, including some ground objects near the tower. One  
Figure 8: Illustration of 2D detection results on airborne LiDAR data.  
Figure 9: Illustration of 3D detection results on airborne LiDAR data.  
potential solution is to enhance the YOLO framework utilized in this study by updating it to the latest version, which is integrated with the SAM (segment anything model) [30]. This upgrade is expected to yield more precise profiles of TTs in virtual views, effectively mitigating background noise. Alternatively, increasing the number of virtual views by generating multiple perspectives at varying angles could provide a more comprehensive constraint on the profile of TTs. This additional information would facilitate the 3D region growing algorithm in distinguishing and excluding background points. Future research endeavors will concentrate on these strategies for a comprehensive investigation, aiming to enhance the algorithm's robustness.  
Conceptualization, J.T.; methodology, J.T. and L.Z.; software, L.Z.; experiment and validation, J.F.; draft preparation, L.Z. and J.F.; review and editing, J.T., J.F. and G.S.; funding: J.T. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by a project from the China Electric Power Research Institute: \"Research on Key Technologies for Intelligent Identification, Quantitative Inversion, and 3D Modeling of Transmission Channel Environment Based on Satellite Remote Sensing\" (project number: GY83-23-019).  
Not applicable.  
The TTPLA dataset presented in this study are openly available in TTPLA_dataset at [[https://github.com/R3](https://github.com/R3) ab/ttpla_dataset]([https://github.com/R3](https://github.com/R3) ab/ttpla_dataset) (accessed on 15 December 2024), reference number [6]. Other contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.