Aerial Image Analysis Using Deep Learning for Electrical Overhead Line Network Asset Management

Electricity networks are critical infrastructure, delivering vital energy services. Due to the significant number, variety and distribution of electrical network overhead line assets, energy network operators spend millions annually on inspection and maintenance programmes. Currently, inspection inv...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Anicetus Odo, Stephen McKenna, David Flynn, Jan Bernd Vorstius
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Pattern recognition machine learning image classification asset management power transmission Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/4900481ba89b472781484045e2b45338
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:4900481ba89b472781484045e2b45338
record_format	dspace
spelling	oai:doaj.org-article:4900481ba89b472781484045e2b453382021-11-09T00:01:08ZAerial Image Analysis Using Deep Learning for Electrical Overhead Line Network Asset Management2169-353610.1109/ACCESS.2021.3123158https://doaj.org/article/4900481ba89b472781484045e2b453382021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9585716/https://doaj.org/toc/2169-3536Electricity networks are critical infrastructure, delivering vital energy services. Due to the significant number, variety and distribution of electrical network overhead line assets, energy network operators spend millions annually on inspection and maintenance programmes. Currently, inspection involves acquiring and manually analysing aerial images. This is labour intensive and subjective. Along with costs associated with helicopter or drone operations, data analysis represents a significant financial burden to network operators. We propose an approach to automating assessment of the condition of electrical towers. Importantly, we train machine learning tower classifiers without using condition labels for individual components of interest. Instead, learning is supervised using only condition labels for towers in their entirety. This enables us to use a real-world industry dataset without needing costly additional human labelling of thousands of individual components. Our prototype first detects instances of components in multiple images of each tower, using Mask R-CNN or RetinaNet. It then predicts tower condition ratings using one of two approaches: (i) component instance classifiers trained using class labels transferred from towers to each of their detected component instances, or (ii) multiple instance learning classifiers based on bags of detected instances. Instance or bag class predictions are aggregated to obtain tower condition ratings. Evaluation used a dataset with representative tower images and associated condition ratings covering a range of component types, scenes, environmental conditions, and viewpoints. We report experiments investigating classification of towers based on the condition of their multiple insulator and U-bolt components. Insulators and their U-bolts were detected with average precision of 96.7 and 97.9, respectively. Tower classification achieved areas under ROC curves of 0.94 and 0.98 for insulator condition and U-bolt condition ratings, respectively. Thus we demonstrate that tower condition classifiers can be trained effectively without labelling the condition of individual components.Anicetus OdoStephen McKennaDavid FlynnJan Bernd VorstiusIEEEarticlePattern recognitionmachine learningimage classificationasset managementpower transmissionElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 146281-146295 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Pattern recognition machine learning image classification asset management power transmission Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Pattern recognition machine learning image classification asset management power transmission Electrical engineering. Electronics. Nuclear engineering TK1-9971 Anicetus Odo Stephen McKenna David Flynn Jan Bernd Vorstius Aerial Image Analysis Using Deep Learning for Electrical Overhead Line Network Asset Management
description	Electricity networks are critical infrastructure, delivering vital energy services. Due to the significant number, variety and distribution of electrical network overhead line assets, energy network operators spend millions annually on inspection and maintenance programmes. Currently, inspection involves acquiring and manually analysing aerial images. This is labour intensive and subjective. Along with costs associated with helicopter or drone operations, data analysis represents a significant financial burden to network operators. We propose an approach to automating assessment of the condition of electrical towers. Importantly, we train machine learning tower classifiers without using condition labels for individual components of interest. Instead, learning is supervised using only condition labels for towers in their entirety. This enables us to use a real-world industry dataset without needing costly additional human labelling of thousands of individual components. Our prototype first detects instances of components in multiple images of each tower, using Mask R-CNN or RetinaNet. It then predicts tower condition ratings using one of two approaches: (i) component instance classifiers trained using class labels transferred from towers to each of their detected component instances, or (ii) multiple instance learning classifiers based on bags of detected instances. Instance or bag class predictions are aggregated to obtain tower condition ratings. Evaluation used a dataset with representative tower images and associated condition ratings covering a range of component types, scenes, environmental conditions, and viewpoints. We report experiments investigating classification of towers based on the condition of their multiple insulator and U-bolt components. Insulators and their U-bolts were detected with average precision of 96.7 and 97.9, respectively. Tower classification achieved areas under ROC curves of 0.94 and 0.98 for insulator condition and U-bolt condition ratings, respectively. Thus we demonstrate that tower condition classifiers can be trained effectively without labelling the condition of individual components.
format	article
author	Anicetus Odo Stephen McKenna David Flynn Jan Bernd Vorstius
author_facet	Anicetus Odo Stephen McKenna David Flynn Jan Bernd Vorstius
author_sort	Anicetus Odo
title	Aerial Image Analysis Using Deep Learning for Electrical Overhead Line Network Asset Management
title_short	Aerial Image Analysis Using Deep Learning for Electrical Overhead Line Network Asset Management
title_full	Aerial Image Analysis Using Deep Learning for Electrical Overhead Line Network Asset Management
title_fullStr	Aerial Image Analysis Using Deep Learning for Electrical Overhead Line Network Asset Management
title_full_unstemmed	Aerial Image Analysis Using Deep Learning for Electrical Overhead Line Network Asset Management
title_sort	aerial image analysis using deep learning for electrical overhead line network asset management
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/4900481ba89b472781484045e2b45338
work_keys_str_mv	AT anicetusodo aerialimageanalysisusingdeeplearningforelectricaloverheadlinenetworkassetmanagement AT stephenmckenna aerialimageanalysisusingdeeplearningforelectricaloverheadlinenetworkassetmanagement AT davidflynn aerialimageanalysisusingdeeplearningforelectricaloverheadlinenetworkassetmanagement AT janberndvorstius aerialimageanalysisusingdeeplearningforelectricaloverheadlinenetworkassetmanagement
_version_	1718441381083480064

Aerial Image Analysis Using Deep Learning for Electrical Overhead Line Network Asset Management

Ejemplares similares