摘要
为实现输电线路中无人机对隐患图像缺陷的自动识别,提出一种基于先验知识快速区域卷积神经网络(Faster R-CNN)的输电线路无人机图像识别方法。该方法基于先验框的设计和迁移学习的思想对Faster R-CNN图像识别模型进行改进,有效提高了模型的识别准确率和泛化性。试验结果表明,所提方法在不同拍摄条件下和故障类型下均能够准确迅速地识别判断故障,具有优异的识别性能。
To achieve automatic identification of defect images in transmission lines by UAV,a method based on prior knowledge faster region convolutional neural network(Faster R-CNN)for transmission line UAV image recognition is proposed.Faster R-CNN image recognition model is improved based on the design of prior boxes and the concept of transfer learning,effectively enhancing the model's recognition accuracy and generalization capability.The experimental results show that the proposed method can identify faults accurately and quickly under different shooting conditions and fault types,and has excellent identification performance.
作者
霍红刚
周蠡
蔡杰
贺兰菲
陈然
何峰
王灿
HUO Honggang;ZHOU Li;CAI Jie;HE Lanfei;CHEN Ran;HE Feng;WANG Can(State Grid Hubei Electric Power Co.,Ltd.,Wuhan 430070,China;State Grid Hubei Economic&Technology Research Institute,Wuhan 430077,China;Hubei Keneng Power&Electronics Co.,Ltd.,Wuhan 430073,China;College of Electrical Engineering and New Energy,China Three Gorges University,Yichang 443002,China)
出处
《智慧电力》
北大核心
2024年第6期108-115,共8页
Smart Power
基金
国家自然科学基金资助项目(52107108)
