电器与能效管理技术 ›› 2022, Vol. 0 ›› Issue (10): 38-43.doi: 10.16628/j.cnki.2095-8188.2022.10.006

• 研究与分析 • 上一篇    下一篇

一种适用输电线路智能巡检的人工智能图像识别方法

饶成成, 罗李毅, 徐健儿   

  1. 广东电网责任有限公司 机巡管理中心, 广东 广州 510160
  • 收稿日期:2022-02-25 出版日期:2022-10-30 发布日期:2023-01-04
  • 作者简介:饶成成(1992—),男,工程师,主要从事机巡缺陷智能识别研究。|罗李毅(1978—),男,高级经济师,主要从事系统经济运行工作。|徐健儿(1988—),男,会计师,主要从事机巡缺陷智能识别工作。

An Artificial Intelligence Image Recognition Method Suitable for Intelligent Inspection of Transmission Line

RAO Chengcheng, LUO Liyi, XU Jianer   

  1. Machine Patrol Management Center of Guangdong Power Grid Co.,Ltd.,Guangzhou 510160,China
  • Received:2022-02-25 Online:2022-10-30 Published:2023-01-04

摘要:

特征提取作为处理输电线路航拍巡检图像最重要阶段之一,传统方法对故障或目标的识别准确率不高且单一,耗时较长,且易受到背景、形态及材料等因素的影响,难以应用于实际中。为解决上述传统方法缺陷,引入深度学习,采用一种基于区域的全卷积网络,利用标注数据训练其网络,并利用在线困难样本挖掘、样本优化、软性非极大值抑制等改进方法进行优化。实验结果表明,所提方面在定位目标更快更准确,应用在输电线路巡检的检测精度较高,以满足输电线路智能巡检的需求。

关键词: 深度学习, 图像识别, 基于区域的全卷积网络, 目标检测, 输电线路

Abstract:

Feature extraction is one of the most important stages of processing aerial patrol images of transmission lines.The recognition accuracy of fault or target by the traditional methods is not high,single,time-consuming,and easy to be affected by the factors such as background,shape and material,so it is difficult to be applied in practice.In order to solve the defects of the above traditional methods,the deep learning is introduced,a region based full convolution network is adopted.The network is trained with labeled data,and the improved methods such as online difficult sample mining,sample optimization and soft non maximum suppression are used for optimization.The experimental results show that the proposed aspects are faster and more accurate in locating targets.The detection accuracy applied in transmission line patrol inspection is high,so as to meet the needs of transmission line intelligent patrol inspection.

Key words: deep learning, image recognition, region based full convolution network, target detection, transmission line

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