Edge Detection of Composite Insulators Hydrophobic Image Based on Improved Canny Operator

doi:10.4236/epe.2013.54B114

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Energy and Power Engineering, 2013, 5, 593-596

doi:10.4236/epe.2013.54B114 Published Online July 2013 (http://www.scirp.org/journal/epe)

Edge Detection of Composite Insulators Hydrophobic

Image Based on Improved Canny Operator

Kang Yan, Fochi Wang , Zhongyuan Zhang, Ningcai Li, Fangcheng Lv

Hebei Provincial Key Laboratory of Power Transmission Equipment Security Defense,

North China Electric Power University, Baoding, China

Email: [email protected]

Received April, 2013

ABSTRACT

The detection of hydrophobicity is an important way to evaluate the performance of composite insulators, which is

helpful to the safe operation of composite insulators. Image processing technology is used to judge the hydrophobicity

of composite insulators, which makes detection results more accurate and overcomes the subjective drawbacks of tradi-

tional detection methods. As the traditional Canny operator requires manual intervention in selecting the variance of the

Gaussian filter and the threshold, the paper presents a method of edge detection based on improved Canny operator.

First, the adaptive median filter replaces the Gaussian filter, which can eliminate the impact from the variance of Gaus-

sian filter and remove noise according to the characteristics of the image itself. Then the Ostu threshold method is used

to select the best threshold automatically, which makes the edge detection be more continuous and reduce the presence

of fake edges. The results show that the operator is applicable to all hydrophobic images.

Keywords: Composite Insulators; Hydrophobicity; Canny Operator; Edge Detection; Adaptive Median Filter; Ostu

Threshold Method

1. Introduction

Composite insulators with good anti-pollution flashover

performance have been widely used in the system of

electric power. The excellent performance of composite

insulators is mainly benefited from their own hydropho-

bicity and hydrophobicity transference. In the actual op-

eration, the hydrophobicity of the composite insulators

maybe descend or even lost, due to the influence of oper-

ating years, environmental changes and their own aging

factors, which may lead to pollution flashover of trans-

mission line [1]. Therefore, it is necessary to detect the

hydrophobicity of the composite insulators.

There are three methods detecting the hydrophobicity

of the composite insulators: static contact angle method,

surface tension method and spray grading method [2]. As

static contact angle method and surface tension method

are very strict requirements on the test environment, they

are mainly used to evaluate the conditions of composite

insulators in the laboratory [3]. Spray grading method is

simple, rapid, and has been used in electrical system

widely. However, its detection results are different for

different operators. At present, digital image processing

technology is applied to detect the hydrophobicity of

composite insulators, which overcomes the different de-

tection results caused by human subjectivity, and makes

the detection results more objective and accurate[4]. So

some scholars begin to study it and achieve some results.

However, these results are only suitable for the compos-

ite insulators whose backgrounds are clean, not very

good to detect composite insulators with complex back-

grounds. Considering the contamination and light, the

paper presents an edge detection method of composite

insulator hydrophobicity based on improved Canny op-

erator, which is applicable to all hydrophobic images.

2. The Principle of Canny Operator

The edge detection method of Canny operator is to find

topical maximum value of the image gradient, the gradi-

ent is calculated by the derivative of the Gauss filter.

Canny operator should satisfy the three judgment criteria:

signal-to-noise ratio criterion, positioning accuracy crite-

rion and single-edge response criterion [5].

1) Signal-to-noise ratio criterion: the probability of the

non-edge points is judged to be the edge points or edge

points are judged to be non-edge points is low; mathe-

matical expression as follows:





Gxfxdx

SNR

xdx

















(1)

K. YAN ET AL.

594

Among them,



x is the impulse response of filter

in boundary







 ; is edge function;



Gx



the variance of Gauss noise. The signal-to-noise ratio is

larger; the quality of the edge detection is higher.

2) Positioning accuracy criterion: the detected edge is

as far as possible in the actual edge center; mathematical

expression as follows:





Gxfxdx

Localization

xdx

















(2)

Among them, and



Gx





xare the first deriva-

tives of and



Gx





x, the value of

is greater, the positioning accuracy is higher.

Localization

3) Single edge criterion: single edge is responded by

only one pixel response. And the false response should

be controlled as much as possible; mathematical expres-

sion as follows:

 



fxdx

Df fxdx



























(3)

Among them, is the average distance be-

tween two zero cross points.



Canny operator is the best edge detection operator

based on three criteria. The process of the Canny opera-

tor is shown in Figure 1.

3. Defects of Canny Operator

In order to achieve de-noising purposes, the traditional

Canny operator uses Gaussian filter to smooth image.

Gaussian Smooth function as follows:

(, )exp[]

Gxy









(4)

It can be seen that the variance (δ) has a significant

impact on smoothing image. If the δ was not appropriate,

it may cause edge deletion.

The threshold of Canny operator also need artificial

selection. If the threshold is improper, it will also cause

edge absence or presence of false edges, which can’t

satisfy the real-time requirements in practical engineering.

Figure 1. Canny operator flow chart.

4. The Edge Detection Algorithm of

Improved Canny Operator

Aiming at the problem of Canny operator in selecting

filter and threshold, the author introduces the adaptive

median filter to instead Gauss filter, and uses the method

of adaptive threshold setting to instead of artificial selec-

tion.

The improvements overcome the drawbacks of tradi-

tional detection methods which need artificial selection

in selection of the Gaussian filter variance and threshold.

4.1. Adaptive Median Filter

The principle of median filter is a point’s value being

replaced of the mean value of each point in the point

field [6]. The neighborhood which uses a pixel for center

point needs to be determined when the median filter

processes an image, which is called for window. When

the window is moved in the image, the noise of image

can be removed.

Adaptive median filter is based on the median filter.

Although they are all using a rectangular area as a win-

dow, adaptive median filter is based on the degree of

noise interference adaptively adjusting the filter window

size [7].

is a window of a image. Defined as follows:

min



Minimum gray of

max



Maximum gray of

med



Median gray of



Gray of the point(x, y)

max



The maximum window of

Adaptive median filtering consists of the first layer

(Level A) and the second layer (Level B). The main al-

gorithm is shown as follows:

Level A: 1mmed in





2mmed ax





If 1 and 2

0A0A



, then go to the Level B. Oth-

erwise, increase the window size.

If the window sizemax



, the Level A is repeated.

Otherwise, output

Level B: 1mxy

BZ in





2mxyax

BZ Z





If and

10B20B



, then output

, Otherwise,

output .

med

Adaptive median filter not only removes noise ac-

cording to the characteristics of the image itself, but also

retains the details of edge, which makes the fuzzy region

become clearer and improves the image quality.

4.2. Otsu Threshold is Used to Obtain the Thre-

shold

Otsu threshold method is an adaptive threshold determi-

K. YAN ET AL. 595

nation method, which is derived from the principle of

least squares method [8] .With this method, an optimal

threshold () of an image can be obtained. The pixels

of the entire image can be divided into the foreground

and background with the , the difference between the

foreground and background is the biggest by now. The

process of optimal threshold () acquisition is as follows:

Assuming the gray value of an image is 1, .

The numbers of pixel with gray value is , then the

total numbers of pixel:

2,3m

N



(5)

The probability of each gray value:

(1,2,3

)m

(6)

Using the gradation value , the image can be di-

vided into foreground 0

W and background 1. The

0 contains all the pixels of gray values

those are less than or equal to . The 1

(1,2, 3)W

kk (1





contains all the pixels of gray values

those are greater than . The L0 and L1 is the sum of

the foreground pixels and the background pixels:

2, 3)kk m

;

iik

LnL









(7)

The and is the probability of and :

;

iik

wpw









(8)

The and are averages gray values of

and :

unp



w (9)

unp







(10)

The class separation distance of image:

01 01

()( )kwwuu



 (11)

Obviously, the class separation distance of image is

greater, the segmentation effect is better. The optimal

threshold as follows:

max( )





 (12)

Otsu threshold method is introduced to the Canny op-

erator, which makes the selection of image threshold

have the adaptability. They can select the most appropri-

ate threshold to detect the image edge according to their

own characteristics, and the detected edges are continu-

ous and fewer false.

5. Experimental Results and Analysis

The steps of the improved Canny edge detection algo-

rithm as follows:

1) The adaptive median filter is used to reduce the

noise of the image;

2) Using the finite difference of the first-order partial

derivatives to calculate the gradient magnitude and direc-

tion;

3) The gradient amplitude and non-maxima are sup-

pressed, only retains local maxima;

4) Using the Otsu threshold method to determine the

optimal threshold value;

5) Using the optimal threshold to detect and connect

edges.

In order to verify the validity of the algorithm, a large

number of hydrophobic images are used to do the ex-

periment in the paper. The treatment effects of the algo-

rithm on HC1 ~ HC7 hydrophobic images are shown in

Figure 2.

Original image Canny Improved Canny

Figure 2. The results of HC1-HC7 image edge detection.

K. YAN ET AL.

596

aditional canny operator is improved

[1] Z. C. Guan, et r Transmissio

The experimental results show that the improved [2]

anny operator solves the lack of boundary due to the

transparency of water, and makes the continuity of the

detected edge be better than the traditional canny opera-

tor. At the same time, the numbers of few false edges are

being reduced.

6. Conclusions

In the paper, the tr

in selecting the variance of the Gaussian filter and the

threshold, which overcomes the drawbacks of manual

intervention in selecting the variance of the Gaussian

filter and the threshold. The test results show that the

improved canny operator is suitable for detecting the

water on the polluted insulator. It not only makes the

continuity of the detected edge be stronger, but also re-

duces the probability of false edge.

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