Accurately segmenting the catenary is essential for extracting its components and detecting geometric parameters.In fact,the catenary scene is complex,with significant differences in size between internal components.There are many compo-nents with similar and connected semantic information,which makes it difficult for existing deep learning methods to accurately complete catenary point cloud semantic segmentation tasks.To address this issue,this paper proposes a neural network named GDM-Net that leverages graph convolution and multi-level features.GDM-Net includes a graph-based local feature extractor that enhances local feature extraction of the catenary point cloud,a double efficient channel attention module that considers the extraction of global and salient features of the catenary point cloud,and a refinement module of multi-scale feature fusion that improves segmentation accuracy by extracting and fusing multi-scale information of the catenary.The proposed network significantly improves the point cloud segmentation ability of catenary components,particularly at the intersection.Based on qualitative and quantitative analysis of the overhead catenary system dataset,the method is verified to achieve the highest accu-racy among five other point cloud deep learning methods.The OA,mIoU,and F1 accuracy indices reach 96.73％,91.06％,and 95.28％,respectively.Qualitative comparisons demonstrate that the proposed network effectively reduces the misclassifi-cation problem of component links and improves the integrity of catenary component segmentation.
维普
万方数据