基于深度卷积神经网络的输电线路可见光图像目标检测

周筑博; 高佼; 张巍; 王晓婧; 张静

doi:10.3788/YJYXS20183304.0317

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基于深度卷积神经网络的输电线路可见光图像目标检测

图像处理 | 更新时间：2020-07-06

- 基于深度卷积神经网络的输电线路可见光图像目标检测
- Object detection of transmission line visual images based on deep convolutional neural network
- 液晶与显示 2018年33卷第4期页码：317-325
- 作者机构：
  
  1.天津航天中为数据系统科技有限公司(天津市智能遥感信息处理技术企业重点实验室), 天津 300301
  2.济南汤尼机器人科技有限公司, 山东济南 250101
  3.南方电网科学研究院有限责任公司, 广东广州 510080
- 作者简介：
  
  [ "周筑博(1990-), 男, 山东潍坊人, 硕士, 工程师, 主要从事机器学习及电网应用方面的工作。E-mail:zhouzhubo90@163.com" ]
- 基金信息：
- DOI：10.3788/YJYXS20183304.0317
  中图分类号： TP751.1
- 收稿日期：2017-12-04，
  
  录用日期：2017-12-31，
  
  纸质出版日期：2018-04-05
- 稿件说明：
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周筑博, 高佼, 张巍, 等. 基于深度卷积神经网络的输电线路可见光图像目标检测[J]. 液晶与显示, 2018,33(4):317-325.

Zhu-bo ZHOU, Jiao GAO, Wei ZHANG, et al. Object detection of transmission line visual images based on deep convolutional neural network[J]. Chinese journal of liquid crystals and displays, 2018, 33(4): 317-325.
周筑博, 高佼, 张巍, 等. 基于深度卷积神经网络的输电线路可见光图像目标检测[J]. 液晶与显示, 2018,33(4):317-325. DOI： 10.3788/YJYXS20183304.0317.

Zhu-bo ZHOU, Jiao GAO, Wei ZHANG, et al. Object detection of transmission line visual images based on deep convolutional neural network[J]. Chinese journal of liquid crystals and displays, 2018, 33(4): 317-325. DOI： 10.3788/YJYXS20183304.0317.

摘要

为了检测输电线路可见光图像中的塔材、玻璃绝缘子和复合绝缘子，本文采用了一种基于深度卷积神经网络的技术。通过有人直升机搭载高清相机拍摄19条不同的输电线路近600张图片，对图片中的背景、塔材、玻璃绝缘子和复合绝缘子目标进行人工标注及分块，采用数据扩展生成包含15万个样本的输电线路图像库。构造5层深度卷积神经网络，首先用Cifar-100数据集对网络进行预训练，然后用输电线路图像库进行网络调优。本文方法在检测真阳率为90%时，假阳率低于10%，明显优于传统方法，可用于输电线路可见光图像中的塔材、玻璃绝缘子和复合绝缘子检测，检测结果可用于诊断参考或进一步的目标状态分析。可对输电线路可见光图像中的塔材和绝缘子目标进行检测，并可扩展到其它类型目标的检测。

Abstract

A deep convolutional neural network based method is adopted to detect objects such as tower

glass insulator and composite insulator in visible images of transmission lines. About 600 visible images of 19 different transmission lines are captured by manned helicopter with high-definition camera. All of the images are then annotated manually and segmented into blocks with 4 different labels:background

tower

glass insulator and composite insulator. These blocks are then augmented to around 150 000 training samples which comprise the transmission line image dataset. A five-layer deep convolutional neural network is designed and pre-trained by using Cifar-100 dataset

the trained network is then fine-tuned by using transmission line image dataset. The experimental results show that when detection true positive rate is 90%

the false alarm rate is less than 10%

which is obviously superior to the traditional methods. It can be used for the detection of tower

glass insulator and composite insulator in visible images of transmission lines. The detection result can be used as reference for diagnosis or state analysis of transmission lines. This method can be used to detect tower and insulator in visible images of transmission lines

and can be extended to detect other typical objects.

关键词

Keywords

references

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