Publication: Detection and Monitoring of Power Line Corridor from Satellite Imagery Using RetinaNet and K-Mean Clustering
| dc.citedby | 3 | |
| dc.contributor.author | Haroun F.M.E. | en_US |
| dc.contributor.author | Deros S.N.M. | en_US |
| dc.contributor.author | Din N.M. | en_US |
| dc.contributor.authorid | 57218938188 | en_US |
| dc.contributor.authorid | 57188721836 | en_US |
| dc.contributor.authorid | 9335429400 | en_US |
| dc.date.accessioned | 2023-05-29T09:11:16Z | |
| dc.date.available | 2023-05-29T09:11:16Z | |
| dc.date.issued | 2021 | |
| dc.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 | en_US |
| dc.description.abstract | 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 | en_US |
| dc.description.nature | Article in Press | en_US |
| dc.identifier.doi | 10.1109/ACCESS.2021.3106550 | |
| dc.identifier.scopus | 2-s2.0-85113323754 | |
| dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113323754&doi=10.1109%2fACCESS.2021.3106550&partnerID=40&md5=c09c82c25852a82656d1f9cc7cdf34af | |
| dc.identifier.uri | https://irepository.uniten.edu.my/handle/123456789/26503 | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.relation.ispartof | All Open Access, Gold | |
| dc.source | Scopus | |
| dc.sourcetitle | IEEE Access | |
| dc.title | Detection and Monitoring of Power Line Corridor from Satellite Imagery Using RetinaNet and K-Mean Clustering | en_US |
| dc.type | Article | en_US |
| dspace.entity.type | Publication |