PNLNet: Partial Supervision‐Driven Nonlocal‐Local Network for Weakly Supervised Semantic Segmentation of Substation Point Clouds

ABSTRACT Existing semantic segmentation algorithms for substation point clouds typically rely on fully supervised learning, which requires a large amount of manually labelled point cloud data. This makes the segmentation process both time‐consuming and costly. To address this issue, this paper proposes PNLNet, a pseudo‐label‐based weakly supervised learning algorithm. The network comprises two core components: a semi‐supervised pseudo‐label generation module and a point cloud segmentation main network. The pseudo‐label generation module employs a semi‐supervised strategy that combines a local‐nonlocal relational graph with a graph convolutional network. This strategy performs a semi‐supervised graph node classification task to generate point‐level pseudo‐labels for the point cloud data. The main network adopts an encoder‐decoder architecture, incorporating a Convolutional Block Attention Module (CBAM) to enhance feature representation across different channels and extract critical information from diverse locations. Additionally, the network uses an inverted residual multi‐layer perceptron (InvResMLP) to mitigate overfitting and gradient vanishing issues. Furthermore, a substation point cloud dataset was constructed for this study and the PNLNet algorithm was evaluated through ablation studies and comparative experiments with state‐of‐the‐art fully supervised and weakly supervised learning methods. Experimental results demonstrate that PNLNet achieves segmentation performance comparable to the best fully supervised algorithms while reducing annotation time by 90%. The model significantly lowers the time and cost associated with processing substation point cloud data, maintaining high segmentation accuracy.