基于知识图谱的电力杆塔主要构件识别方法研究

Research on a recognition method of main components of electric power towers using knowledge graph

电力杆塔主要构件的图像识别是无人机巡检的主要内容,准确识别杆塔构件对保障电网运行具有重要价值。为此,提出一种基于深度学习和知识图谱的电力杆塔主要构件识别方法。首先,建立不同构件类型的拓扑关系,形成杆塔空间知识图谱;其次,设计语义关系推理模型,融合构件语义特征与拓扑关系,得到增强特征;最后,拼接增强特征与原始特征,实现特征融合。实验表明:在未架线电力杆塔多目标识别方面,所提方法比Reasoning-RCNN、Cascade-RCNN及Faster-RCNN的识别效果好,能够精准识别杆塔主要构件,对无人机电力巡检具有参考价值。

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.