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Abstract：
Semantic segmentation of target objects in power transmission line corridor point cloud scenes is a crucial step in powerline tree barrier detection. The massive quantity, disordered distribution, and non-uniformity of point clouds in power transmission line corridor scenes pose significant challenges for feature extraction. Previous studies have often overlooked the core utilization of spatial information, limiting the network\'s ability to understand complex geometric shapes. To overcome this limitation, this paper focuses on enhancing the deep expression of spatial geometric information in segmentation networks and proposes a method called BDF-Net to improve RandLA-Net. For each input 3D point cloud data, BDF-Net first encodes the relative coordinates and relative distance information into spatial geometric feature representations through the Spatial Information Encoding block to capture the local spatial structure of the point cloud data. Subsequently, the Bilinear Pooling block effectively combines the feature information of the point cloud with the spatial geometric representation by leveraging its bilinear interaction capability thus learning more discriminative local feature descriptors. The Global Feature Extraction block captures the global structure information in the point cloud data by using the ratio between the point position and the relative position, so as to enhance the semantic understanding ability of the network. In order to verify the performance of BDF-Net, this paper constructs a dataset, PPCD, for the point cloud scenario of transmission line corridors and conducts detailed experiments on it. The experimental results show that BDF-Net achieves significant performance improvements in various evaluation metrics, specifically achieving an OA of 97.16%, a mIoU of 77.48%, and a mAcc of 87.6%, which are 3.03%, 16.23%, and 18.44% higher than RandLA-Net, respectively. Moreover, comparisons with other state-of-the-art methods also verify the superiority of BDF-Net in point cloud semantic segmentation tasks.
摘要：
输电线路走廊点云场景中目标对象的语义分割是电力线树屏障检测的关键步骤。大量的，无序分布，输电线路走廊场景中点云的非均匀性对特征提取提出了重大挑战。以往的研究往往忽视了空间信息的核心利用,限制了网络理解复杂几何形状的能力。为了克服这个限制,本文着眼于增强分割网络中空间几何信息的深度表达，并提出了一种称为BDF-Net的方法来改进RandLA-Net。对于每个输入的3D点云数据，BDF-Net首先通过空间信息编码块将相对坐标和相对距离信息编码为空间几何特征表示,以捕获点云数据的局部空间结构。随后，双线性池块通过利用其双线性相互作用能力有效地将点云的特征信息与空间几何表示相结合，从而学习更多的区别性局部特征描述符。全局特征提取块利用点位置与相对位置的比值捕获点云数据中的全局结构信息，从而增强网络的语义理解能力。为了验证BDF-Net的性能,本文构建了一个数据集，PPCD,针对输电线路走廊的点云场景进行了详细的实验。实验结果表明，BDF-Net在各种评估指标上实现了显著的性能提升，具体实现97.16%的OA，77.48%的mIoU，mAcc为87.6%，为3.03%，16.23%,比RandLA-Net高18.44%，分别。此外，与其他最新方法的比较也验证了BDF-Net在点云语义分割任务中的优越性。
