Ricardo Feliciano dos Santos

Diogo Lavado, Cláudia Soares, Alessandra Micheletti et al.

Research on supervised learning algorithms in 3D scene understanding has risen in prominence and witness great increases in performance across several datasets. The leading force of this research is the problem of autonomous driving followed by indoor scene segmentation. However, openly available 3D data on these tasks mainly focuses on urban scenarios. In this paper, we propose TS40K, a 3D point cloud dataset that encompasses more than 40,000 Km on electrical transmission systems situated in European rural terrain. This is not only a novel problem for the research community that can aid in the high-risk mission of power-grid inspection, but it also offers 3D point clouds with distinct characteristics from those in self-driving and indoor 3D data, such as high point-density and no occlusion. In our dataset, each 3D point is labeled with 1 out of 22 annotated classes. We evaluate the performance of state-of-the-art methods on our dataset concerning 3D semantic segmentation and 3D object detection. Finally, we provide a comprehensive analysis of the results along with key challenges such as using labels that were not originally intended for learning tasks.

Diogo Lavado, Ricardo Santos, Andre Coelho et al.

Ensuring the safety and reliability of power grids is critical as global energy demands continue to rise. Traditional inspection methods, such as manual observations or helicopter surveys, are resource-intensive and lack scalability. This paper explores the use of 3D computer vision to automate power grid inspections, utilizing the TS40K dataset -- a high-density, annotated collection of 3D LiDAR point clouds. By concentrating on 3D semantic segmentation, our approach addresses challenges like class imbalance and noisy data to enhance the detection of critical grid components such as power lines and towers. The benchmark results indicate significant performance improvements, with IoU scores reaching 95.53% for the detection of power lines using transformer-based models. Our findings illustrate the potential for integrating ML into grid maintenance workflows, increasing efficiency and enabling proactive risk management strategies.