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Detection and Monitoring of Power Line Corridor from Satellite Imagery Using RetinaNet and K-Mean Clustering

Antennas; Deep learning; Electric power transmission; K-means clustering; Satellite imagery; Unmanned aerial vehicles (UAV); Vegetation; Airborne photography; Current monitoring; Electrical transmission; Identification algorithms; K-mean clustering; K-mean clustering algorithm; Monitoring system; Po...

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Main Authors: Haroun F.M.E., Deros S.N.M., Din N.M.
Other Authors: 57218938188
Format: Article
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-265032023-05-29T17:11:16Z Detection and Monitoring of Power Line Corridor from Satellite Imagery Using RetinaNet and K-Mean Clustering Haroun F.M.E. Deros S.N.M. Din N.M. 57218938188 57188721836 9335429400 Antennas; Deep learning; Electric power transmission; K-means clustering; Satellite imagery; Unmanned aerial vehicles (UAV); Vegetation; Airborne photography; Current monitoring; Electrical transmission; Identification algorithms; K-mean clustering; K-mean clustering algorithm; Monitoring system; Power interruptions; Monitoring Monitoring of electrical transmission towers (TTs) is required to maintain the integrity of power lines. One major challenge is monitoring vegetation encroachment (VE) that can cause power interruption. Most of the current monitoring techniques use unmanned aerial vehicles (UAV) and airborne photography as an observation medium. However, these methods are expensive and not practical for monitoring wide areas. In this paper, we introduced a new method for monitoring power line corridor from satellite imagery. The proposed method consists of two stages. In the first stage, we used the existing state-of-the-art RetinaNet deep learning (DL) model to detect the locations of the TTs from satellite imagery. A routing algorithm has been developed to create a path between every adjacent detected TT. In addition to the routing algorithm, a corridor identification algorithm has been established for extracting the power line corridor area. In the second stage, we used, the k-mean clustering algorithm was used to highlight the VE regions within the power line corridor area after converting the target satellite image into hue, saturation, and value (HSV) color space. The proposed monitoring system was able to detect TTs from satellite imagery with a mean average precision (mAP) of 72.45% for an Intersection of Union (IoU) threshold of 0.5 and 85.21% for IoU threshold of 0.3. Also, the monitoring system successfully discriminate the high- and low-density vegetation regions from satellite imagery. Author Article in Press 2023-05-29T09:11:16Z 2023-05-29T09:11:16Z 2021 Article 10.1109/ACCESS.2021.3106550 2-s2.0-85113323754 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113323754&doi=10.1109%2fACCESS.2021.3106550&partnerID=40&md5=c09c82c25852a82656d1f9cc7cdf34af https://irepository.uniten.edu.my/handle/123456789/26503 All Open Access, Gold Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Antennas; Deep learning; Electric power transmission; K-means clustering; Satellite imagery; Unmanned aerial vehicles (UAV); Vegetation; Airborne photography; Current monitoring; Electrical transmission; Identification algorithms; K-mean clustering; K-mean clustering algorithm; Monitoring system; Power interruptions; Monitoring
author2 57218938188
author_facet 57218938188
Haroun F.M.E.
Deros S.N.M.
Din N.M.
format Article
author Haroun F.M.E.
Deros S.N.M.
Din N.M.
spellingShingle Haroun F.M.E.
Deros S.N.M.
Din N.M.
Detection and Monitoring of Power Line Corridor from Satellite Imagery Using RetinaNet and K-Mean Clustering
author_sort Haroun F.M.E.
title Detection and Monitoring of Power Line Corridor from Satellite Imagery Using RetinaNet and K-Mean Clustering
title_short Detection and Monitoring of Power Line Corridor from Satellite Imagery Using RetinaNet and K-Mean Clustering
title_full Detection and Monitoring of Power Line Corridor from Satellite Imagery Using RetinaNet and K-Mean Clustering
title_fullStr Detection and Monitoring of Power Line Corridor from Satellite Imagery Using RetinaNet and K-Mean Clustering
title_full_unstemmed Detection and Monitoring of Power Line Corridor from Satellite Imagery Using RetinaNet and K-Mean Clustering
title_sort detection and monitoring of power line corridor from satellite imagery using retinanet and k-mean clustering
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806427787845697536
score 13.222552

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