The image recognition of the main components of electric power towers is a primary focus of UAV inspec-tions,as accurately identifying these tower components holds significant value for ensuring the smooth operation of power grids.To address this need,the paper proposes a method for recognizing the main components of electric power towers based on deep learning and knowledge graph.Firstly,the paper establishes topological relationships between component types,forming a spatial knowledge graph of the towers.Subsequently,it designs a model for se-mantic relationship inference that integrates semantic features of components with their topological relationships,re-sulting in feature enhancement.Finally,by concatenating these enhanced features with the original features,feature fusion is achieved.Experimental results demonstrate that the proposed method outperforms Reasoning-RCNN,Cascade-RCNN,and Faster-RCNN in the multi-target recognition of unstrung towers.It enables precise recognition of the main tower components,thus offering valuable insights for UAV-based power line inspections.
关键词
深度学习/电力杆塔/智能识别/知识图谱/Reasoning-RCNN
Key words
deep learning/electric power tower/intelligent recognition/knowledge graph/Reasoning-RCNN
维普
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