The stable and reliable supply of electric power is strongly related to the normal social production. In recent years, power transmission lines inspection based on remote sensing methods has made great progress, especially using the UAV-borne LiDAR system. The extraction and identification of power transmission lines (i.e., conductors) from 3D point clouds are the basis of LiDAR data-based power grid risk management. However, existing rule-based/traditional machine learning extraction approaches have exposed some limitations, such as the lack of timeliness and generalization. Moreover, the potential of deep learning is seriously overlooked in RoW (Right of Way) LiDAR inspection tasks. Thus, we proposed DCPLD-Net: a diffusion coupled convolution neural network for real-time power transmission lines detection from UAV-borne LiDAR data. To implement efficient 2D convolution on 3D point clouds, we proposed a novel point cloud representation, named Cross Section View (CSV), which transforms the discrete point clouds into 3D tensors constructed by voxels with a deformed geometric shape along the flight trajectory. After the CSV feature generation, the encoded features of each voxel are treated as energy (i.e., heat) signals and diffused in space to generate diffusion feature maps. The feature of the power line points are thus enhanced through this simulated physical process (diffusion). Finally, a single-stage detector named PLDNet is proposed for the multiscale detection of conductors on the diffused CSV representations. The experimental results show that the DCPLD-Net achieves an average F1 score of 97.14 % at 8 Hz detection frequency on RoWs inspection LiDAR datasets collected by both mini-UAV LiDAR and large-scale fully autonomous UAV power lines inspection robots, and surpasses compared methods (i.e. PointNet ++, RandLA-Net) in terms of F1 scores and IoU.

电力的稳定可靠供应与社会正常生产密切相关。近年来，基于遥感方法的输电线路检测取得了长足的进步，尤其是使用无人机载激光雷达系统。输电线路的提取和识别（即，来自 3D 点云的导体）是基于 LiDAR 数据的电网风险管理的基础。然而，现有的基于规则/传统的机器学习提取方法已经暴露了一些局限性，例如缺乏及时性和泛化性。此外，深度学习的潜力在 RoW（Right of Way）激光雷达检测任务中被严重忽视。因此，我们提出了 DCPLD-Net：一种扩散耦合卷积神经网络，用于从无人机载激光雷达数据中实时检测电力传输线。为了在 3D 点云上实现高效的 2D 卷积，我们提出了一种新的点云表示，称为横截面视图 (CSV)，它将离散点云转换为由沿飞行轨迹具有变形几何形状的体素构建的 3D 张量。在 CSV 特征生成之后，，热）信号并在空间中扩散以生成扩散特征图。因此，通过这种模拟的物理过程（扩散）增强了电力线点的特征。最后，提出了一种名为 PLDNet 的单级检测器，用于在扩散的 CSV 表示上对导体进行多尺度检测。实验结果表明，DCPLD-Net 在小型无人机 LiDAR 和大型全自主无人机电力线检测机器人收集的 RoWs 检测 LiDAR 数据集上以 8 Hz 检测频率实现了 97.14 % 的平均 F1 分数，并超过了比较方法（即 PointNet ++，RandLA-Net）在 F1 分数和 IoU 方面。