智慧电力2024,Vol.52Issue(6) :108-115.

基于先验知识Faster R-CNN的输电线路无人机图像识别方法

UAV Image Recognition Method for Transmission Line Based on Prior Knowledge Faster R-CNN

霍红刚 周蠡 蔡杰 贺兰菲 陈然 何峰 王灿
智慧电力2024,Vol.52Issue(6) :108-115.

基于先验知识Faster R-CNN的输电线路无人机图像识别方法

UAV Image Recognition Method for Transmission Line Based on Prior Knowledge Faster R-CNN

霍红刚 1周蠡 2蔡杰 2贺兰菲 2陈然 2何峰 3王灿4
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作者信息

  • 1. 国网湖北省电力有限公司,湖北武汉 430070
  • 2. 国网湖北经济技术研究院,湖北武汉 430077
  • 3. 湖北科能电力电子有限公司,湖北武汉 430073
  • 4. 三峡大学电气与新能源学院,湖北宜昌 443002
  • 折叠

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.

Abstract

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.

关键词

无人机/图像识别/Faster/R-CNN/先验知识

Key words

UAV/image recognition/Faster R-CNN/prior knowledge

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基金项目

国家自然科学基金(52107108)

出版年

2024
智慧电力
陕西省电力公司

智慧电力

CSTPCDCSCD北大核心
影响因子:0.831
ISSN:1673-7598
参考文献量29
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