The Deep Learning Based Power Line Defect Detection System Built on Data Collected by the Cablewalker Drone

Titov, Evgenii; Limanovskaya, O. V.; Lemekh, Alexandr; Volkova, Daria

doi:10.1109/sibircon48586.2019.8958397

Cited by 11 publications

(4 citation statements)

References 4 publications

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“…Many works have been done to detect transmission and distribution towers using UAV in [28] the authors used LS-Net fully CNN to detect transmission line from UAV image as well [29] used You Only Look Once (YOLO) DL algorithms to detect and localize transmission towers with different sizes in images taken by a cable walker drone. Also YOLO became a very popular method for object detection and localization in both normal and satellite images [30].…”

Section: Machine Learning Methodsmentioning

confidence: 99%

A Review of Vegetation Encroachment Detection in Power Transmission Lines using Optical Sensing Satellite Imagery

Haroun¹

2020

IJATCSE

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Vegetation encroachment in power transmission lines can cause outages, which may result in severe impact on economic of power utilities companies as well as the consumer. Vegetation detection and monitoring along the power line corridor right-of-way (ROW) are implemented to protect power transmission lines from vegetation penetration. There were various methods used to monitor the vegetation penetration, however, most of them were too expensive and time consuming. Satellite images can play a major role in vegetation monitoring, because it can cover high spatial area with relatively low cost. In this paper, the current techniques used to detect the vegetation encroachment using satellite images are reviewed and categorized into four sectors; Vegetation Index based method, object-based detection method, stereo matching based and other current techniques. However, the current methods depend usually on setting manually serval threshold values and parameters which make the detection process very static. Machine Learning (ML) and deep learning (DL) algorithms can provide a very high accuracy with flexibility in the detection process. Hence, in addition to review the current technique of vegetation penetration monitoring in power transmission, the potential of using Machine Learning based algorithms are also included.

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Section: Machine Learning Methodsmentioning

confidence: 99%

A Review of Vegetation Encroachment Detection in Power Transmission Lines using Optical Sensing Satellite Imagery

Haroun¹

2020

IJATCSE

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“…In order to discover the closed corridor area, the algorithm draws two parallel lines (lines 12-17) to create a closed surface as shown in Figure 7. After determining the corridor area, the algorithm stores all the closed surface points (lines 18-21), sorts the points in a clockwise manner, and draws a contoured surface around the corridor area (lines [23][24][25][26].…”

Section: Transmission Tower Routing and Corridor Extractionmentioning

confidence: 99%

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

2021

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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.INDEX TERMS Deep learning, K-mean, satellite images, transmission tower detection.

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“…Moreover, accurate segmentation can only be achieved in ideal situations, while in actual aerial images, the colors of the power lines and the background may be very similar. Recently, deep learning-based methods [10][11][12] have also been gradually applied to power line detection, Titov et al [10] built a defect detection system in blocks, and yolov3 is used for detecting and classifying power line poles in images or videos. Pan et al [11] raised a power line extraction network combined with the encoder-decoder framework to extract power lines automatically with an introduced self-attention block and an introduced multiscale feature enhancement block.…”

Section: Introductionmentioning

confidence: 99%

Direction Consistency‐Guided Lightweight Power Line Detection Network for Aerial Images

Zhang,

Shu,

Zhu

et al. 2023

Journal of Sensors

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Accurate detection of power lines in aerial images is of great significance in ensuring grid security. However, complex power line scenarios and the thin and light structure of power lines both make it difficult to detect power lines accurately. Most of the existing approaches use traditional deep learning methods, using networks with a large number of parameters, computation, and memory occupation, thus making them not lightweight enough to perform on mobile devices. Based on this, a lightweight power line detection network based on direction consistency and location attention is proposed. The network is designed with a coordinate-aware feature extraction layer, which performs feature extraction by four-layer stacking to achieve faster inference speed while ensuring the network has fewer parameters. This layer is also able to sense the coordinates of the center pixel of the convolution in the image during the convolution process, thus preserving the location information of the power lines. In order to enhance the power line representation, a two-stage context-guided module is later utilized to simultaneously learn local features, surrounding context, and global context. Then, the features are input into a Gaussian kernel estimation module and features are aggregated in the corresponding directions through Gaussian kernels of eight different directions. The main directions of the power lines in the image and the corresponding Gaussian convolution kernels are obtained by filtering the feature responses. In addition, a kernel-guided decoder module is proposed to take advantage of the estimated power line features in the main direction of Gaussian kernel aggregation. This module can effectively enhance the power line representation and maintain the continuity of power lines. Meanwhile, low-level features are introduced to recover the edge details to realize high performance in distinguishing dense power lines. Both ablation experiments and comparison experiments on the transmission towers and power lines aerial-image and Power Line Aerial Image Dataset show that the proposed power line detection network has a good segmentation performance in complex scenarios. The proposed method performs the best in the comparison experiments, improving over the suboptimal method by 3.51% on average for the max F-measure metric.

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The Deep Learning Based Power Line Defect Detection System Built on Data Collected by the Cablewalker Drone

Cited by 11 publications

References 4 publications

A Review of Vegetation Encroachment Detection in Power Transmission Lines using Optical Sensing Satellite Imagery

A Review of Vegetation Encroachment Detection in Power Transmission Lines using Optical Sensing Satellite Imagery

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

Direction Consistency‐Guided Lightweight Power Line Detection Network for Aerial Images

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