Abstract
Monitoring of electrical transmission towers (TTs) is required to maintain the integrity of power lines. One major challenge is monitoring vegetation encroachment 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 the 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, the k-mean clustering algorithm has been 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 was able to successfully discriminate high- and low-density vegetation regions within the power line corridor area.
Highlights
Electricity is of major importance to modern life, where a power outage can pose risk to livelihood and economy
Most of the previous works in power transmission lines detection and monitoring depend on high-resolution unmanned aerial vehicles (UAV) and aerial images that cannot rapidly cover wide areas compared with satellite images [6]–[12]
Since the transmission towers (TTs) are difficult to be observed from satellite image, the shadows of TTs are incorporated with their body structure and labeled as one unit to provide more information for the convolutional neuron network (CNN)
Summary
Electricity is of major importance to modern life, where a power outage can pose risk to livelihood and economy. These methods are time-consuming, expensive in relation to the coverage area, and unsuitable for rough terrains [2], [3]. Using high-resolution satellite imagery is a promising solution for monitoring power transmission lines. Satellite imagery provides data for a wide coverage area with relatively low cost compared with other optical remote sensing (ORS) methods. Most of the previous works in power transmission lines detection and monitoring depend on high-resolution UAV and aerial images that cannot rapidly cover wide areas compared with satellite images [6]–[12]. F. Mahdi Elsiddig Haroun et al.: Detection and Monitoring of Power Line Corridor From Satellite Imagery. We introduced a new automatic technique to monitor the power line corridor right-of-way from satellite imagery using the RetinaNet deep learning (DL) model and k-mean clustering
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