Towards the Automatic Visual Monitoring of Electricity Pylons from

Aerial Images

Anicetus Odo

, Stephen McKenna

, David Flynn

and Jan Vorstius

School of Science & Engineering, University of Dundee, Dundee, DD1 4HN, Scotland, U.K.

School of Engineering & Physical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, Scotland, U.K.

Keywords:

Visual Inspection, Electricity Pylons, Transfer Learning, Unmanned Aerial Vehicles.

Abstract:

Visual inspection of electricity transmission and distribution networks relies on ﬂying a helicopter around

energized high voltage towers for image collection. The sensed data is taken ofﬂine and screened by skilled

personnel for faults. This poses high risk to the pilot and crew and is highly expensive and inefﬁcient. This

paper reviews work targeted at detecting components of electricity transmission and distribution lines with

attention to unmanned aerial vehicle (UAV) platforms. The potential of deep learning as the backbone of

image data analysis was explored. For this, we used a new dataset of high resolution aerial images of medium-

to-low voltage electricity towers. We demonstrated that reliable classiﬁcation of towers is feasible using deep

learning methods with very good results.

1 INTRODUCTION

Aerial surveillance of electricity network components

is currently an active area of research. We review

recent work on vision-based inspection of electric-

ity network components from aerial images. We then

demonstrate a novel application of deep learning for

tower image classiﬁcation. Speciﬁcally, we classify

towers as being either suspension (S-type) or ten-

sion (T-type) towers. This classiﬁcation, in terms of

tower conﬁguration, will be useful as a step in the in-

spection of other tower parameters, e.g., components

around the cross-arm (insulators, clamps, shackles,

etc.). Tower classiﬁcation is achieved by ﬁrst clas-

sifying each of the multiple images of a tower and

then using voting to determine the tower class. Since

many images do not contain the relevant part of the

tower, we introduce a third unknown (U) image class,

and train 3-class image classiﬁers. Identiﬁcation of U

images, which tend to be of the body and leg regions

of a tower, is a useful step in the inspection of con-

crete muffs, warning signs, vegetation cover and anti-

climbing devices, which are localized around these re-

gions.

2 MOTIVATION

Globally, societies depend on continuity of service

from critical systems such as electrical networks.

Electrical networks support other critical services like

transportation, telecommunications, food, water and

healthcare. Electricity is generated and transmitted

over a system of transmission and distribution net-

work infrastructure. The networks are made up of

high rising metal towers or pylons and span hundreds

of thousands of kilometres (Andersson et al., 2005;

Jones, 2005) along rivers, lakes, hills and lowlands

and sometimes across dense vegetation (Liu et al.,

2015). To ensure effective management , a set of stan-

dards is put in place and regulated. One such regulator

is the Ofﬁce of Gas and Electricity Markets (Ofgem)

in the UK. The major role of this body is to ensure the

enforcement of a uniform level of performance from

all the distribution network operators (DNOs) within

the industry (McGonigle, 2017).

To operate within these standards, DNOs make

huge investment in asset acquisition and management.

The United States alone requires about $2 trillion in-

vestment for upgrades by 2030 (Bronski et al., 2015).

There was over £16 billion of investment on elec-

tricity networks from 2010 and £34 billion needed

up to 2020 (DECC, 2015). This trend is likely to

continue in the coming decade as most of the trans-

mission and distribution network infrastructure has

served the better part of its lifespan. Disruptions re-

ported in Europe, Asia and America within the last

ﬁfteen years (Bakshi, A., Velayutham, A., Srivastava,

2012; Schmidthaler & Reichl, 2016) point to the fact

that electricity assets are aging and in need of constant

monitoring. Making this situation worse is the in-

creasing demand for energy. To mitigate the huge loss

566

Odo, A., McKenna, S., Flynn, D. and Vorstius, J.

Towards the Automatic Visual Monitoring of Electricity Pylons from Aerial Images.

DOI: 10.5220/0009345005660573

In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP, pages

566-573

ISBN: 978-989-758-402-2; ISSN: 2184-4321

in ﬁnances and patronage associated with such fail-

ures, evolving new and effective management tools

has become inevitable.

The current state-of-the-art for inspection of elec-

tricity transmission and distribution assets relies on

visual aerial images. These are usually collected from

helicopters (Matikainen et al., 2016). Other sensing

alternatives like light detection and ranging (LiDAR)

are in use. The deployment on helicopters has safety

shortcomings and high operational costs. The current

advancement in UAV technology for remote sensing

offers advantages and has increasingly gained pop-

ularity in aerial photography and surveillance. The

beneﬁts of UAVs include their ﬂexibility (Herwitz

et al., 2004) and low cost of operation (Cai & Walker,

2010).

Given the large numbers of components and in-

spection parameters, a robust data management archi-

tecture is needed. Solutions should combine histor-

ical data, current conditions and energy demand to

advise on a preferred course of action. There is an

ongoing discussion towards Big Data architecture for

smart grid (IEEE-Smart-Grid, 2017) and the role of

machine learning and artiﬁcial intelligence is promi-

nent.

3 LITERATURE REVIEW

Electricity assets are the valuable components (tan-

gible and intangible) of the network, which are in-

tegral to the proﬁtable delivery of the services that

businesses depend upon (Clarke, 2011). These in-

clude transformers, high voltage towers, conductors,

insulators, suspension clamps, connecting links, etc.

For an asset to remain relevant, its health state should

be inspected for signs of failure or degradation, so

as to maximize availability, performance and reliabil-

ity. Eyre-Walker et al. (2015) presented application

of advanced condition assessment and asset manage-

ment techniques for overhead electricity network as-

set monitoring to involve data acquisition and analy-

sis.

Regular and effective inspection and management

requires high ﬁnancial commitment from owners and

operators in the industry. This has triggered increased

collaboration with researchers to ﬁnd improved and

cost-effective ways of conducting power-line inspec-

tion (Martinez et al., 2014). A major direction is im-

provement of alternative sensing platforms and the

drive to automate the process (Matikainen et al.,

2016).

Various events along power distribution lines may

lead to power outages. The most common causes

of outages are: (1) failure of power-line components

(Larsson & Ek, 2004) and (2) interference with sur-

rounding vegetation (Andersson et al., 2005). Causes

of the 2003 major grid blackouts in North America

and Europe included inadequate vegetation manage-

ment (i.e. tree trimming). The use of line men for

checking the encroachment of trees along power dis-

tribution lines is still practiced today. This is not only

costly but inefﬁcient. Remote alternatives have been

introduced (Ahmad et al., 2015; Zhang et al., 017b).

The inspection of speciﬁc components accounts

for the largest propotion of inspection tasks. These

include conductors (Zhang et al., 017a; Chen et al.,

2016; Sharma et al., 2014; Li et al., 2010), tow-

ers (Martinez et al., 2014) and insulators (Oberweger

et al., 2014; Li et al., 2012; Salustiano et al., 2014).

3.1 UAV Navigation

Safe ﬂight of a UAV along a power-line corridor is key

to successful inspection. Although a pilot is dedicated

to this task, there have been crashes due to system,

human or environmental factors. System and human

errors can be controlled but environmental impacts

could come from several sources, e.g. gust wind.

To solve the problem posed by gust wind, Liu et al.

(2015) proposed the creation of a no-ﬂy-zone along

the distribution network corridor using GPS coordi-

nates of the towers.

Sa et al. (2015) demonstrated the use of vertical

take-off and landing of UAVs for the inspection of

pole-like structures. They combined monocular, in-

ertia and sonar data for navigation information and

Extended Kalman Filters to maintain a safe distance

from the pole even in the presence of environmental

disturbances. Essentially, this is a detect and follow

algorithm.

Golightly & Jones (2005) combined Hough trans-

form and Kalman ﬁlters to guide a rotorcraft along

detected powerlines. A follow-up study (Jones et al.,

2006) used an air vehicle simulator (AVS) to demon-

strate that visual data can be used to determine, and

hence regulate vehicle position relative to the over-

head lines. Cer

on et al. (2018) developed a system

that detects and follows powerlines from images.

3.2 Obstacle Detection and Avoidance

The detection of obstacles such as vegetation and

buildings along powerlines has been investigated

(Zhang et al., 2012, 017a,b). Low altitude photogram-

metry has been explored in these studies to extract

3D point clouds of the power-line corridor. The dis-

tance between the powerlines and the 3D point cloud

Towards the Automatic Visual Monitoring of Electricity Pylons from Aerial Images

567

is taken as a criterion for automatically locating ob-

stacles. Zhang et al. (2012) used monocular measure-

ment and inertia to estimate the position of landmarks

as well as the position and orientation of the UAVs.

3.3 Detection of Towers

Detection of electricity pylons was studied by (Dutta

et al., 2015) and (Jiang et al., 2017). The main

contribution of (Dutta et al., 2015) was to mini-

mize clutter due to heterogeneous background using

optimized mean shift-based segmentation. The re-

sulting image was divided into a grid of rectangu-

lar patches called granules. The best granules were

selected using gradient density and cluster density-

based thresholding. The clusters corresponding to py-

lon regions within key granules were merged through

shared boundary criterion. Finally, pylons were de-

tected using context information. Results were en-

couraging. On the other hand, Jiang et al. (2017) ex-

plored the use of an unmanned aerial vehicle (UAV)

for outdoor data acquisition. They achieved this using

an oblique photogrammetric system integrated with a

low-cost double-camera imaging system, an on-board

dual-frequency Global Navigation Satellite System

(GNSS) receiver and a ground master GNSS station

in ﬁxed position.

The use of UAVs in a cooperative way was pro-

posed by (Pirbodaghi et al., 2015). This system used

two robotic platforms that were heterogeneous and

cooperative in executing tasks. While a rob-on-wire

inspected the lines by moving on them, an octocopter

served as a wireless relay node establishing data trans-

fer between rob-on-wire and the ground station and

carried out inspection at the same time on the towers.

In addition to the detection of towers, there is a

need to identify defects in its components. A method

for estimating corrosion on towers was presented in

(Tsutsumi et al., 2009). It was based on a support

vector machine using the radial basis function kernel.

Some synthetic images using colour temperature and

brightness were added to augment the training data.

This was evaluated using 1,427 images of 8 towers.

Detection of other defects on towers has not been ad-

equately explored.

3.4 Detection of Insulators

Detection of insulators and insulator defects has been

studied (Oberweger et al., 2014; Zhai et al., 2017;

Liu et al., 2017). Saliency and adaptive morphol-

ogy were the bases for insulator fault detection. Liu

et al. (2017) detected insulators and hammers using

a multi-layer perceptron. Jabid & Ahsan (2018) de-

tected insulators using rotation invariant local direc-

tional pattern (RI-LDP) features. These features were

used by an SVM to classify regions of insulator and

predict their faults.

3.5 Detection of Conductors

The detection of conductors has been addressed in

several studies. In (Yang et al., 2012), video frames

were binarised through an adaptive thresholding ap-

proach and a Hough transform was used to detect

line candidates. This was followed by a fuzzy C-

means clustering algorithm to discriminate the con-

ductor lines from other detected line patterns like

roads, river banks and vegetation. Li et al. (2008) used

a pulse coupled neural network ﬁlter to remove back-

ground noise from images prior to Hough transform

being employed to detect straight lines. Thereafter,

knowledge-based line clustering was applied to re-

ﬁne the detection results. Bhujade (2013) and Sharma

et al. (2014) suppressed the natural surroundings (re-

gions of sky and vegetation) and used a Hough trans-

form. In (Tian et al., 2015), conductors were extracted

based on directional constraints using a double-side

ﬁlter, and an improved Hough transform with parallel

constraint was used for conductor recognition. Their

results show signiﬁcant improvement because of the

addition of direction and parallelism constraints. Sim-

ilarly, Zhu et al. (2013) presented a double-side ﬁlter-

based conductor recognition method for a UAV vi-

sion system. This method was based on linear ob-

ject enhancement and parallel lines constraints as in

(Tian et al., 2015). A Radon transform was used to

ﬁnd the parallel lines. Real-time detection of con-

ductors from video was presented in (Liu & Mejias,

2012). Ippolito et al. (2016) also showed a real-time

method but with 3D scanning using LiDAR. This uti-

lized a voxel-based method with a series of classiﬁers

to identify and reconstruct conductors. A mini UAV

mounted with LiDAR was proposed in (Santos et al.,

2017) for sensing the powerline corridor.

Most studies reviewed in this section focus on de-

tecting conductors without considering defect detec-

tion or analysis. Zhang, F. et al. (2016) presented a

technique to detect and remove fog from an image

to enhance detection. Zhai et al. (2017) compared

the capability of three edge detection algorithms us-

ing images of towers. Xie et al. (2017) suggested the

use of multiple sensors from a large UAV. Qin et al.

(2018) based their approach on a cable inspection

robot to improve the payload and power capabilities

of their inspection platform. Menendez et al. (2016)

presented a simulation of a UAV-based line tracking

system and mounted a visual sensor on a robotic arm

VISAPP 2020 - 15th International Conference on Computer Vision Theory and Applications

568

Figure 1: Top: images of S-type towers. Middle: images of T-type towers. Bottom: images from which tower type is not

apparent.

that detected and tracked lines.

In summary, some components of the power-line

corridor including towers, insulators and conductors

have been studied in the literature. The use of com-

puter vision techniques is popular and several ma-

chine learning algorithms (e.g. multi-layer percep-

tron, pulse coupled neural network) have been used.

The potential of deep learning as the backbone for

analyzing the sensed data has not been sufﬁciently

covered partly due to lack of suitably labelled data.

Nguyen et al. (2018) and Zhang et al. (2018) have

highlighted the huge potential of this approach. Re-

cent success of deep learning for the detection and

classiﬁcation of objects directly from images presents

an exciting opportunity for real-time inspection of

components of electricity transmission and distribu-

tion networks.

4 CLASSIFICATION OF TOWERS

FROM AERIAL IMAGES

4.1 Data Formation

The dataset used for this study was collected using he-

licopters mounted with high resolution cameras. Each

image has 5616x3744 pixels. The images are of tow-

ers from low-to-medium voltage lines. Each line is

identiﬁed by a unique line number (e.g. A54, A74).

Along each line are multiple towers (e.g. A54(002),

A54(003), etc).

Images are taken of each tower from different

views (e.g. right and left circuit) across the crossarm,

body and foot regions. All the images of a tower are

grouped into a tower ’bag’ with a unique identiﬁer

(tower number). Each tower bag has been inspected

and labelled by an expert. It is important to empha-

size that although multiple images of each tower were

acquired, a single label has been assigned to the entire

bag. Some suspension (S-type) towers are shown in

Figure 1 (row 1), i.e., different tower structures with

suspended cables. Row 2 of Figure 1 shows some

T-type (tension) towers. These towers also have dif-

ferent structures with cables pulling on the structure.

With respect to how the data is formed, most image-

based classiﬁcation and detection problems (datasets)

have one label per image. Here, tower images were

captured to have good representation of the compo-

nents and conditions but labelled as a bag. This bulk

labelling presents the following challenges: (1) In

each tower bag there are a mix of cross-arm images

(rows 1 and 2 of Figure 1) and images of body and

leg regions (row 3 of Figure 1); (2) Images of tower

body or leg regions (row 3 of Figure 1) have no fea-

tures identifying them as S-type or T-type. Therefore,

attempting to classify such images independently as

being of S-type or T-type towers will cause errors.

To address the problem of bulk label assignment,

all images of tower body/leg regions were labelled as

U images (unknown). At image level, there are thus

three class labels: S, T, and U.

It is required that images found in the training set

are not present in the testing set. Considering that in

our dataset each tower has several images (bag of ex-

amples), training and testing sets have been assigned

tower-wise.

There are 28231 images for training, 4593 for val-

idation and 4240 images for testing. The breakdown

of towers and images across class labels is presented

in Table 1.

Towards the Automatic Visual Monitoring of Electricity Pylons from Aerial Images

569

Figure 2: Examples of test images that were correctly classiﬁed. Top: S class. Middle: T class. Bottom: U class.

Table 1: Distribution of data for training, validation and

testing sets.

Towers (Number) Images (Number)

Training

S-type (519) S (12621)

U (3102)

T-type (270) T (9166)

U (3343)

Validation

S-type (79) S (1963)

U (709)

T-type (41) T (1469)

U (452)

Testing

S-type (80) S (1829)

U (551)

T-type (39) T (1215)

U (645)

4.2 Training

We ﬁne-tuned a VGG16 network (Simonyan & Zis-

serman, 2015) using ImageNet weight initialization.

We replace the fully connected layers with a new fully

connected output layer with 3 nodes (3 classes). We

also trained from scratch, a ResNet with 86 layers and

based on pre-activation of residual modules (He et al.,

2016).

The images are 5616x3744x3 in size (colour).

They were resized to 244x244x3 to ﬁt our target input

shape. The input images were randomly augmented

and fed into the model. To ensure that the model sees

different sets of images each time they were sampled,

we applied width and height shifts, zooming and ﬂip-

ping. The model was optimized using Stochastic Gra-

dient Descent (SGD) with learning rate of 1e-3.

4.3 Evaluation

There are 118 towers comprising of 4240 images in

the test set. The distribution of towers and images for

testing is shown in Table 1. The VGG-based classiﬁer

predicted 97.04%, 97.69% and 96.32% of S, T and U

test images correctly. The ResNet classiﬁed 96.99%,

96.54% and 95.65% of S, T and U test images cor-

rectly.

Figure 2 shows some examples of S, T, and U im-

ages that were correctly classiﬁed. Figure 3 shows

examples of incorrect classiﬁcations. Comparing the

results, one notices that close-range images with rel-

atively clean backgrounds are correctly classiﬁed.

Some characteristics of the incorrectly classiﬁed im-

ages are (1) long-range images, (2) heavy background

clutter, e.g. houses, trees, (3) instances of multiple ob-

jects e.g., Figure 3, row 1, image 3, and (4) cases of

wrong labels e.g., Figure 3, row 3, images 3, 4 and 5.

4.4 Voting Mechanism for Tower Level

Classiﬁcation

An aim of this study is to classify towers as suspen-

sion (S-type) or tension (T-type). Each tower is pre-

sented as a bag of images. Within each bag are 20-

30 instances. We use a majority voting mechanism.

This samples all the image predictions for each bag

and counts the number of occurrences of S and T la-

bels. The label with the highest count is returned as

the ﬁnal prediction for the bag (i.e. tower level classi-

ﬁcation). We envisaged a situation in which there is a

tie (equal predictions of targets). However, there was

no tie in our experiments. As shown in Table 2, the

VGG-based model misclassiﬁed one S-type tower as

VISAPP 2020 - 15th International Conference on Computer Vision Theory and Applications

570

(a) S images misclassiﬁed as T

(b) T images misclassiﬁed as S

Figure 3: Examples of test images incorrectly classiﬁed.

T-type. On the other hand, ResNet predicted all the

towers correctly as shown in Table 3.

Table 2: Fine-tuned VGG model: Confusion matrix with

majority voting for tower level classiﬁcation.

Predictions

S-type T-type

Actual

S-type 79 1

T-type 0 38

Table 3: ResNet: Confusion matrix with majority voting for

tower level classiﬁcation.

Predictions

S-type T-type

Actual

S-type 80 0

T-type 0 38

5 CONCLUSION

We reviewed methods for electricity network asset in-

spection. This included the use of machine learning,

computer vision and the potential of deep learning.

We presented the classiﬁcation of electricity towers

based on their conﬁguration. To the best of our knowl-

edge, there is no previous report of a deep learning-

based classiﬁcation of tower images. Our method

of electricity tower and image classiﬁcation is a pre-

cursor for the inspection of other power-line com-

ponents and condition parameters: (1) inspection of

components around the cross-arm (insulators, clamps,

shackles, conductors, etc.), (2) inspection of concrete

muffs, DODs, tower name plates, etc.

ACKNOWLEDGEMENT

The authors would like to thank the Northern Pow-

ergrid for providing very useful data for this study.

Anicetus Odo is grateful to TETFund for the grant to-

wards his PhD.

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