结合知识图谱和小目标改进的RCNN电力杆塔部件识别方法

doi:10.3778/j.issn.1002-8331.2309-0381

摘要/Abstract

摘要： 电力巡检是输电线路建设中的重要一环，利用无人机对电力杆塔进行巡检，并使用深度学习辅助技术人员进行智能决策，能够减少漏检率，提高巡检效率。已有方法大多无法做到对无人机影像中电力部件进行多尺度识别，或无法适应电力杆塔影像复杂场景。针对以上问题，提出了一种结合知识图谱和小目标改进的RCNN电力杆塔部件识别方法。在Reasoning-RCNN模型基础上引入了空间知识图谱模块，对图像目标框间的空间关系建模；针对小目标问题构建了ROI上下文特征融合模块，并引入基于图像切分的小目标识别策略。对电力杆塔影像数据进行人工标注，并在此数据集上对模型进行实验评估。实验结果表明，所提算法实现了对复杂场景下的电力杆塔部件的多尺度识别，且精度超越了其他基准模型。

关键词: 无人机巡检, 深度学习, 电力杆塔部件识别, 知识图谱, 小目标检测

Abstract: Electric power inspection is an important part of transmission line construction. Using drones to inspect transmission towers and using deep learning technology to assist technicians in making intelligent decisions, can reduce false detection rate and improve detection efficiency. Existing studies are mostly incapable of fully recognizing tower components from all perspectives and scales, or adapting to the complex scenes of transmission tower images. To solve these issues, an RCNN method of transmission tower component detection based on knowledge graph and small object improvement is proposed. Firstly, a spatial knowledge graph module is constructed based on the Reasoning-RCNN model to model the spatial relationships among the detected boxes in the image. Then, an ROI context feature fusion module is constructed to address the small object problem, and a small object detection strategy based on image partitioning is introduced. The image data of transmission tower are manually annotated and the proposed method is evaluated on this dataset. The experimental results show that the proposed method achieves full-scale detection of transmission tower components in complex scenes. The comparison results also demonstrate the superior performance of the proposed method over baseline models.

Key words: drone inspection, deep learning, transmission tower component detection, knowledge graph, small object detection

张锴, 贾涛. 结合知识图谱和小目标改进的RCNN电力杆塔部件识别方法[J]. 计算机工程与应用, 2025, 61(4): 299-309.

ZHANG Kai, JIA Tao. RCNN Method of Transmission Tower Component Detection Based on Knowledge Graph and Small Object Improvement[J]. Computer Engineering and Applications, 2025, 61(4): 299-309.

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