计算机工程与应用2025，Vol.61Issue(4)：299-309,11.DOI:10.3778/j.issn.1002-8331.2309-0381
结合知识图谱和小目标改进的RCNN电力杆塔部件识别方法
RCNN Method of Transmission Tower Component Detection Based on Knowledge Graph and Small Object Improvement
摘要
Abstract
Electric power inspection is an important part of transmission line construction.Using drones to inspect trans-mission 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 compo-nents 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 improve-ment 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 experi-mental 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.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Key words
drone inspection/deep learning/transmission tower component detection/knowledge graph/small object detection分类
信息技术与安全科学引用本文复制引用
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):299-309,11.基金项目
国家自然科学基金(41971332) （41971332）
南方电网公司科技项目(0315002022030201JJ00025). （0315002022030201JJ00025）
