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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Institute of Electrical and Electronics Engineers Inc.
2023
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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 |
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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 |
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57218938188 |
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57218938188 Haroun F.M.E. Deros S.N.M. Din N.M. |
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Article |
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Haroun F.M.E. Deros S.N.M. Din N.M. |
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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 |