The image recognition of the main components of electric power towers is a primary focus of UAV inspections， 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 semantic relationship inference that integrates semantic features of components with their topological relationships， resulting 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.
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