电网机巡图像分析框架与深度学习方法

李远宁; 宁柏锋; 董召杰

doi:10.11959/j.issn.1000-0801.2020056

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电网机巡图像分析框架与深度学习方法

运营技术广角 | 更新时间：2024-06-05

- 电网机巡图像分析框架与深度学习方法
- Patrol image analysis framework and deep learning method for power grid
- 电信科学 2020年36卷第8期页码：167-174
- 作者机构：
  
  1. 南方电网数字电网研究院，广东广州 511458
  2. 深圳供电局有限公司，广东深圳 518000
- 作者简介：
  
  [ "李远宁（1981- ），男，博士，南方电网数字电网研究院高级工程师，主要研究方向为电力大数据、人工智能、信息化" ]
  [ "宁柏锋（1983- ），男，深圳供电局有限公司高级工程师，主要研究方向为电力信息化、电网故障诊断" ]
  [ "董召杰（1985- ），男，南方电网数字电网研究院高级工程师，主要研究方向为电力信息化、人工智能" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2020056
  中图分类号： TP183
- 网络出版日期：2020-08，
  
  纸质出版日期：2020-08-20
- 稿件说明：
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李远宁, 宁柏锋, 董召杰. 电网机巡图像分析框架与深度学习方法[J]. 电信科学, 2020,36(8):167-174.

Yuanning LI, Baifeng NING, Zhaojie DONG. Patrol image analysis framework and deep learning method for power grid[J]. Telecommunications science, 2020, 36(8): 167-174.
李远宁, 宁柏锋, 董召杰. 电网机巡图像分析框架与深度学习方法[J]. 电信科学, 2020,36(8):167-174. DOI： 10.11959/j.issn.1000-0801.2020056.

Yuanning LI, Baifeng NING, Zhaojie DONG. Patrol image analysis framework and deep learning method for power grid[J]. Telecommunications science, 2020, 36(8): 167-174. DOI： 10.11959/j.issn.1000-0801.2020056.

摘要

随着智能制造与物联网技术的发展，无人机被电网企业广泛地应用于输电线路巡视检查，同时也产生了大量的巡检图像数据亟需分析与处理。针对机巡图像分析中面临的多类多尺度、光照变化及遮挡等挑战，设计了一套从用户数据归集、分析与自动标注到用户评价反馈的U2U图像分析框架，在此基础上研究了Faster R-CNN和SSD两种深度学习方法在绝缘子、防震锤、均压环、屏蔽环等电力部件检测中的应用，提出了基于K-means++聚类分析的兴趣对象锚点信息框设定方法。实验结果表明，本文提出的方法有效地提高了深度学习方法对多尺度兴趣部件的适应能力与检测精度，为后续更大规模的机巡图像缺陷检测及机巡深化应用提供了有益的借鉴。

Abstract

With the development of intelligent manufacturing and IoT

UAV has been widely utilized by power grid enterprise in patrolling transmission lines.At the same time

massive patrol image data need to be analyzed urgently.A U2U image analysis framework was designed from user data collection

automatic annotation and to user feedback.Two deep learning methods

which were faster R-CNN and SSD

were explored and applied in U2U to detect five types of power components

including insulators

dampers

grading rings and shielding rings.A refining method based on K-means++ was proposed for the parameters of anchor box.Experimental results demonstrate that the proposed methods can effectively improve the adaptability and detection accuracy of deep learning method for multiple-scale power components and provide a useful reference for subsequent defect detection and deep application of UAV patrolling.

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