热图像分析和分割技术研究水泥和鸟类沉积对太阳能电池板表面温度的影响(IJIGSP-V9-N12-2)

热图像分析和分割技术研究水泥和鸟类沉积对太阳能电池板表面温度的影响(IJIGSP-V9-N12-2)
热图像分析和分割技术研究水泥和鸟类沉积对太阳能电池板表面温度的影响(IJIGSP-V9-N12-2)

I.J. Image, Graphics and Signal Processing, 2017, 12, 12-22

Published Online December 2017 in MECS (https://www.360docs.net/doc/e09282876.html,/) DOI: 10.5815/ijigsp.2017.12.02

Thermal Image Analysis and Segmentation to Study Temperature Effects of Cement and Bird

Deposition on Surface of Solar Panels

Akash Singh Chaudhary 1, D.K. Chaturvedi 2

1,2

Department of Electrical Engineering, Faculty of Engineering, DEI, Dayalbagh, Agra, UP-282005, India

1,2

Email: akashsinghchaudhary@https://www.360docs.net/doc/e09282876.html,, dkc.foe@https://www.360docs.net/doc/e09282876.html,

Received: 08 September 2017; Accepted: 22 September 2017; Published: 08 December 2017

Abstract —To obtain solar energy from solar photo-voltaic panels is not a new task now a days. Solar panels are designed to give their maximum possible output when exposed to the solar radiations. The operating conditions of solar panels affected by the atmospheric conditions like dust, dirt, solar INSOLATION, temperature etc. Here the effect of cement and bird deposits on the surface of solar panels is considered. Temperature rise due to cement and bird deposits develop hot-spots and affects the electrical power output of the solar panel. The cement and bird deposits can be seen by visually on solar panel surface but their effect can not be visualized by naked eye. The infrared THERMOGRAPHY helps to analyze these effects by using thermal imaging camera. This paper focus to study the temperature effects of cement and bird deposition on solar panel surface by capturing thermal images along with analyzing the thermal images using MATLAB digital image processing for better understanding. Image segmentation for cement and bird deposits is performed using watershed transform to achieve the desired region of interest from thermal images.

Index Terms —Bird deposits, Cement deposits, Image segmentation, Solar panels, Temperature effect, Thermal imaging. I. I NTRODUCTION

The area of renewable energy is becoming more popular today due to several drawbacks of the conventional sources of energy like green house gas emission, global warming, pollution etc. Solar energy is one of the preferred area among other non-conventional areas (wind energy, tidal, hydro, biomass, geothermal etc) due to the good availability of solar radiations in India [1]-[2]. Solar energy has been widely used in generation of power, industries, irrigation purpose [3] to fulfill the increment in the generation of energy demand [4]. India has good solar energy of about 2

/74m kWh and

kWh 1510x 5in a day and in a year respectively [5]-[6].

The earth and environment receive

radiation year Joules /10x 6.324 approximately [7]. The

solar solar cell converts these solar radiations into

electrical energy. The solar panels are exposed to the open atmosphere and their surface is affected to many atmospheric conditions. The solar photo-voltaic panels which are subjected to dirt, snow covering, leaves dropping, cement deposits and bird deposits cause partial shading. If these depositions continue to remain present on the panel surface, heating of the affected area starts and may form hot-spots. The overall effect of these depositions either causes a huge power loss or may damage the solar panel. These problems can be avoided using blocking diodes up to certain extent [8]-[9]. The cement deposits and bird deposits are one of the important parameters to be considered when estimating the generated solar power output. The cement and bird deposits increase the temperature of solar cell and decrease the solar power output by a considerable amount. If these depositions remain to continue of solar panel surface, they may cause other problems in the solar cells/ modules like burn or damage of the panel surface. Therefore, the study of cement and bird deposition is important [10]-[11]. The thermal camera captures the

radiated heat emitted by a object above absolute zero temperature. It represents the correct picture of the affected area by capturing thermal images. The thermal images represent the variation of temperature in a colorful pattern called as THERMOGRAMS [12].

II. B ASICS OF S OLAR C ELLS AND S OLAR P ANELS

The energy from sun is obtained in heat form and in light form. A solar cell converts light energy radiated by sun into electrical energy. A solar cell is the smallest element of a solar module, many modules are connected to form a solar photo-voltaic panel. The solar photo-voltaic panels combine to form a photo-voltaic string and many photo-voltaic strings connected together are known as solar photo-voltaic array. The output current and output voltage of a solar cell is known as photo-voltaic

output current and photo-voltaic output voltage. Fig. 1 and fig. 2 show solar cell and solar photo-voltaic panel respectively.

Fig.1. Solar cell

Fig.2. Solar photo-voltaic panel

The basic output current equation of a solar cell can be written as:

???

???+-???

? ?

?--=???

? ?

?sh se

pv pv ηV V O pv

R R I V 1e

I Iph I T D

Where, Vpv is output voltage of photo-voltaic cell, Iph is photo-voltaic generated current, I 0 is reverse saturation current of diode, I D is diode current, V D is voltage drop across diode, Rse is series resistance, Rsh is shunt resistance, V T =(KT/q) is thermal voltage, K is Boltzmann constant, T is cell’s operating temperature in kelvin, q is charge of an electron, ? is quality factor of solar photo-voltaic panel.

The two basic categories of photo-voltaic solar panels used are c rystalline silicon photo-voltaic solar panels and thin film photo-voltaic solar panels. Crystalline silicon photo-voltaic solar panels include Mono-Crystalline and Poly-Crystalline. Thin film photo-voltaic solar panels include Cd Te (Cadmium TELLURIDE), CIGS (Copper Indium Gallium SELENIDE) and a-Si (Amorphous silicon) photo-voltaic solar panels [13].

III. P RINCIPLE O F T HERMAL I MAGING

In 1800 a German Astronomer, Frederick William Herschel discovered Infrared radiations [14]. In thermal imaging the infrared radiations emitted by any object are converted into a different color pattern of thermal images known as THERMOGRAM with the help of thermal camera. These THERMOGRAMS represent the variations of temperature in the thermal image [15]. The following fig. 3 shows the basic components of a thermal imaging system [16].

Fig.3. Basic components of a thermal imaging system

The principle of thermal imaging is

based on the Stefan’s Boltzmann law, which states that the total radiant energy emitted by a black body is directly proportional to the fourth power of the absolute temperature given by equation E r=σ?T 4 (watt/m 2) where E r = Total radiant energy, σ= Stefan’s Boltzmann constant

(5.6697×10-8 W/m 2 K 4), ?= EMISSITIVITY of object, T= Absolute temperature of object. Thermal imaging can be used in monitoring as well as maintenance areas like detection of hot spots, failures, cracks, in solar panels, overheating problems in mechanical installations heating, leakage and insulation breakdown in pipes and valves etc [17].

IV. O PERATION ON M ATLAB D IGITAL I MAGE

P ROCESSING An image (being a two dimensional function f(x,y) and amplitude f) having finite and discrete quantities of x,y and amplitude values of f is known as a digital image. The processing of these digital images by a digital computer is called digital image processing. Digital image processing is an emerging technique used in many fields like engineering, medical, agriculture, education etc. MATLAB is a programming language used for technical computing. Image processing toolbox is one of the frequently used functions in MATLAB [18]. Now a days thermal image processing is also becoming popular due its advantage of having a sharp idea of thermal images through image processing tool in MATLAB. It is useful in determining the high temperature by adding pseudo-colors to the thermal images. The dark colors basically represent the colder portions and bright colors represent the hot portions of the thermal images. The fusion of the original images and thermal images consisting pseudo-colors also used for better understanding of the affected part. The affected areas can be easily detected by using operations of digital image processing. There are many stages and algorithms involved during the processing of a thermal image from initial form to final form [19]-[20]. The digital image processing allows to analyze the captured thermal images against degradation effects, heated areas due to high temperature, quality of image. There techniques use in digital image processing for the analysis of thermal images after the data acquisition involve image filtering, image enhancement, image segmentation, feature extraction etc [21].

The flowchart shown in fig. 4 represents the basic operations used in digital image processing with MATLAB for thermal images analysis.

The fig. 4 shows that first step is to acquire data in the form of thermal image then apply second step of PEE-Processing. Noise is random variation in the color information or brightness in an image, which must be removed. Filtering involves reducing noise from thermal images using filters. Image enhancement involves sharpening, DEBLURRING of image. Next is to perform image segmentation to find the region of interest, edge detection. The feature extraction involves finding parameters like mean, standard deviation, variance, SKEWNESS, KURTOSIS etc. The last step is to classify the image [22]-[23].

There are many methods used for image segmentation like region based segmentation, edge detection, watershed transform segmentation etc. Use of watershed

transform based segmentation is faster and simpler but involves problem of over-segmentation. This problem can be avoided by using markers in watershed transform approach. [24]-[26]. The digital image processing along with thermal imaging is helpful in studying the other fault effects of solar panels like cracks in cell, glass breakage, hot spots in cells, shadowing, broken ribbons, by-pass diode failure in photo-voltaic modules etc [27].

Fig.4 Basic operations used in digital image processing with MATLAB

for thermal images analysis

V. T EMPERATURE E FFECTS OF C EMENT AND B IRD

D EPOSITIONS ON S OLAR P ANELS The cement and bird depositions allow only a part of solar radiations to convert into electricity, rest solar radiations help to increase temperature of the deposited surface area compared to non-deposited surface area and produces heat on the panel surface. This process causes overheating of solar cells which reduces electrical power output and efficiency of solar panels [28]-[30].

The intensity of infrared radiation is a function of temperature of an object, the high is the temperature, higher is the intensity of emitted infrared radiation. Thus area having high temperature has more infrared radiations and high loss of heat. So infrared radiations shows hot-spots (portions having high temperature than other portions) which are not visible by naked eye and hidden portions of heat loss. Therefore temperature of cement and bird depositions is an indicator of solar panel health condition [31]-[32].

VI. T HERMAL I MAGING C AMERA

The thermal imaging camera is a device to capture thermal images of any object without any physical

contact. It uses principle of thermal imaging to detect the thermal radiations/ thermal heat emitted by any object above absolute zero temperature (-273°C). A thermal imaging camera can identify defects which are not visible by naked eye. The dark color in THERMOGRAM shows colder area while bright color shows the hotter area represented in terms of a temperature scale. The maximum temperature, minimum temperature, average temperature of area, temperature of a particular spot in thermal images, region based thermal image analysis can be performed using thermal image analysis software. The fig. 5 shows KUSAM MECO Thermal imaging camera (Model TE-P) used in experiment with its specifications are mentioned in table 1.

Fig.5. KUSAM MECO Thermal Imaging Camera

Table 1. Specifications of thermal camera

VII. W ORK D ONE FOR T EMPERATURE E FFECTS OF C EMENT AND B IRD D EPOSITIONS ON S URFACE OF S OLAR

P ANELS Location for Experiment

The experimental work to study the temperature effects for cement and bird depositions on surface of solar panels was performed on the roof of a building with solar panels installed and facing towards south direction. The building was surrounded by trees in plenty on eastern side and few on western side, there was a building on north side. The following fig. 6 shows the location of solar panels chosen for experiment.

Fig.6. Shows location of solar panels for experiment

Total 72 solar panels were present on the roof of building forming 4 strings connected in parallel. Each string consisting of 18 serially connected solar panels. The specifications of solar module is given as follows: Company: BHEL

Module No.: L20220

P maximum:220 watt power

V open circuit:36 V

I short circuit:8.3 A

V mp:29 V

I mp:7.60 A

Max system voltage: 1000 V

Bypass diode rating: 15 A

Max over current protection: 15 A

STC (Standard test conditions):

INSOLATION: 1000 W/m2

AM: 1.5

Cell Temp.: 25 °C

The measurements for experiment were done on Monday, 11.04.17 with load remain connected to solar panels. The instruments used for measurement were a KUSAM MECO clamp-meter with a THERMOCOUPLE (for current, voltage, temperature measurement), KUSAM MECO thermal imaging camera (for capturing thermal images), and digital solar PYRANOMETER (to measure solar IRRADIANCE). The weather conditions were measured using weather software from system with internet connectivity.

Small pallets of cement deposits and bird beat deposits were observed on few solar panels and rest of the panels were free from any deposit.

The experimental work performed to analyze temperature effects for cement and bird deposition on the surface of solar panels using thermal imaging was divided into two parts.

First part- A is to study temperature effects of cement and bird depositions on surface of solar panels using thermal imaging.

Secondly in part- B to analyze thermal image of cement and bird depositions using thermal image segmentation with MATLAB digital image processing.

A. To study temperature effects of cement and bird depositions on surface of solar panels using thermal imaging: Cement deposits on solar panel surface:

As there was a newly constructed building on the northern side of solar panels, cement deposits were found on solar panels installed adjacent to it only. The cement deposits were irregular in shape and size with small and big pellets. The following fig. 7 shows surface of solar panels with cement deposits.

Fig.7. Shows surface of solar panels string 1with cement deposits On Tuesday, 11.04.17 sunrise and sunset was at 5.58 am and 6.40 pm respectively. The sky was clear with dusty air in the atmosphere. The table 2 given below shows electrical and thermal measurements of cement deposits on solar panel surface for string 1.

Table 2. Measurement parameters of cement deposits on solar panel

surface of string 1

The cement depositions increase temperature of cement deposited area, which causes heating and finally result into loss of electrical power output of solar panel. The table 2 shows that when ambient temperature increases the temperature of three parameters (temperature of cement deposited cell, temperature of cement deposited solar module and temperature of solar panel string 1) increases. This increase in temperature of above three parameters cause heat loss and reduces electrical power output of the cement deposited solar panel string 1.

When ambient temperature decreases, temperature of all three parameters (temperature of cement deposited cell, temperature of cement deposited solar module and temperature of solar panel string 1) also decreases and an increment in electrical power output of solar panel string 1 is noticed. Temperature of cement deposits first increase and then deceases with ambient temperature because the increased temperature of cement deposits causes an increment of temperature of the deposited surface area of solar cell, so the temperature of cement deposits is used to heat the cement deposited solar cell. Again when ambient temperature increases and decreases the temperature of cement deposits increase and decrease with it. So temperature of cement deposits fluctuates with ambient temperature and temperature of cement deposited cell.

The following fig. 8 shows the curves for the temperature variation of cement deposits, cement deposited solar cell, cement deposited solar module and solar panel string 1 with respect to the ambient temperature mentioned in table 2.

Fig.8. Shows curves for temperature variation of cement deposits, cement deposited solar cell, cement deposited solar module and solar panel string 1 with respect to ambient temperature mentioned in table 2 The fig. 8 shows variation of four temperature parameters with ambient temperature. The highest temperature of cement deposits was observed at 51(oC) with corresponding ambient temperature as 39(oC). The points on the curve at which temperature of cement deposits is high, the temperature of cement deposited cell and temperature of cement deposited module is also high. In curve the highest temperature of cement deposited cell was noticed to be 49(oC) with highest ambient temperature 41(oC). Temperature of cement deposited module and solar panel string 1 were highest at 48(oC).

These curves show that temperature of cement deposits, temperature of cement deposited cell, temperature of cement deposited solar module, and temperature of solar panel string 1 are dependent on each other, which affect the electrical power output of solar panels and other degradation effects. The following fig. 9(a) and fig. 9(b) shows normal photograph and thermal image of cement deposits observed on solar panel surface for string 1. The temperature effect of cement deposits was not visible in normal photographs but thermal image shows the heating effect due to high temperature.

(a)

(b)

Fig.9.(a) and fig. 9(b) shows normal photograph and thermal image of cement deposits observed on solar panel surface for string 1

The fig. 9(b) is the thermal image of cement deposits showing a bright yellow colored cement deposit with maximum hot-spot temperature as 59oC and minimum temperature of 50oC. The dark blue color in image shows cold area of solar panel surface. Small pellets of cement deposits are also highlighted with bright yellow color representing hot points of deposits. These heating effects of temperature due to cement deposit are not visible in the normal photograph fig. 9(a).

Bird deposits on solar panel surface:

Fig.10. Shows bird deposits at three places on the surface of solar panel

string 2

The bird deposits were observed at three places on the surface of solar panel string 2, which can be seen in the following fig. 10

The measurements of bird deposit only at one place was done (i.e., for bird deposit shown in fig. 12(a) only) on same day, on Tuesday, 11.04.17 with same atmospheric conditions. The sunrise and sunset was at 5.58 am and 6.40 pm respectively and sky was clear with dusty air. Table 3 shows electrical and thermal measurements of bird deposits on solar panel surface for string 2.

Table 3 Measurement parameters of bird deposit (shown in fig. 12(a)

only) on solar panel surface of string 2

The table 3 shown that the electrical power output of solar panel string 2 decreases with an increase in temperature of bird deposited solar cell and temperature of bird deposited solar module. The electrical power output increases when temperature of bird deposited solar cell and temperature of bird deposited solar module falls. As ambient temperature increases, temperature of bird deposits also increases but after heating surface area of bird deposited solar cell, the temperature of bird deposits get lower. This cycle of increase and decrease in the temperature of bird deposits continues with ambient

temperature, temperature of bird deposited solar cell and temperature of solar panel string 2. The overall effect of temperature of bird deposits reflect on generation of electrical power output of solar panels string 2.

The following fig. 11 shows different curves for temperature variations of bird deposits, bird deposited solar cell, bird deposited solar module and solar panel string 2 with respect to the ambient temperature as mentioned in table 3.

Fig.11. Shows curves for temperature variation of bird deposits, bird deposited solar cell, bird deposited solar module and solar panel string 2 with respect to ambient temperature mentioned in table 3

The fig. 11 shows temperature variation curves of four parameters with ambient temperature. The highest points for temperature of bird deposits was observed at 49(oC) with corresponding ambient temperature as 39(oC). The highest temperature of bird deposited cell was 47(oC) with highest ambient temperature 41(oC). Temperature of bird deposited module and solar panel string 2 were highest at 46(oC). Therefore bird deposits affect temperature of solar panel which result in change in the generation of electrical power output.

The following fig. 12(a), fig. 12(b), fig. 12(c) and fig. 12(d) shows normal photograph and thermal image of bird deposits observed at two places on solar panel surface for string 2.

The fig. 12(a) and fig. 12(c) are normal photographs showing only the bird deposits physically. The fig. 12(b) and fig. 12(d) are the thermal images of these bird deposits showing maximum temperature as 60oC and minimum temperature as 51oC. The hot and cold temperature of bird deposits are represented by bright yellow color and dark blue color in the thermal image of fig. 12(b) and fig. 12(d) respectively.

(a) (b)

(c) (d)

Fig. 12(a) and fig. 12(c) shows normal photograph and fig. 12(b) and fig. 12(d) shows thermal image of bird deposits observed at two places

on solar panel surface for string 2

B. To analyze thermal image of cement and bird depositions using thermal image segmentation with MATLAB digital image processing:

This part of research work deals to find the interested objects in thermal images of cement and bird deposits using image segmentation. The thermal images of cement and bird deposits contain several small deposits. The heated area due to these deposits can be visualize more precisely with the help of image segmentation technique in MATLAB digital image processing. Watershed transform is a powerful method used in image segmentation. Watershed transform has many advantages compared to other image segmentation methods such as simple, INTUTIVE, easy detection, SEGMENTATION and visualization of desired object. But over-segmentation is a big drawback of watershed transform, so to reduce it marker based approach is used. Algorithm used:

In this experiment marker based watershed transform algorithm is used for thermal image analysis of cement and bird deposits. The following figure 13 shows the flowchart of algorithm used in this part of experiment. The marker based watershed algorithm used in experiment can be explained by following steps:

Step 1:First of all thermal image containing cement and bird deposits is captured from thermal imaging camera.

Step 2:Since watershed transform can be applied to gray scale image, so it is obtained from RGB input thermal image.

Step 3:Gradient magnitude of the converted gray scale image is obtained for the desired deposits. Applying watershed transform at this stage gives over-segmentation, so marker approach given in step 4 is used before applying it.

Step 4: Apply morphological operations to clean image by computing foreground and background markers such as opening-by-reconstruction and closing-by-reconstruction.

Step 5:Now apply watershed transform on marked images to get segmented image of desired cement and bird deposits.

Step 6:Obtained the colored watershed transform segmented images of cement and bird deposits.

S tep 7: Visualize the superimposed segmented images of cement and bird deposits for better understanding.

Fig.13. Flowchart of marker based watershed algorithm Watershed transform for cement deposits of thermal image:

After applying marker based watershed algorithm on thermal image fig. 9(b) of cement deposit, the result are shown in fig. 14(a) to fig. 14(f).

(a) (b)

(c) (d)

(e) (f)

Fig.14(a) Shows gray scale image of original thermal image, Fig. 14(b) gradient magnitude image, Fig. 14(c) over-segmentation of watershed transform, Fig. 14(d) markers and object boundaries super imposed on original thermal image, Fig. 14(e) colored segmented watershed transform label matrix, Fig. 14(f) superimposed segmented image of

watershed transform

The watershed transform can only be applied to binary or gray scale image so original RGB thermal image of cement deposit obtained from a thermal camera is converted into gray scale image through MATLAB function rgb2gray. The fig. 14(a) is the converted gray scale image of cement deposit.

After obtaining gray scale image a gradient magnitude is calculated as a segmentation function to detect the image whose dark regions are the desired segmented objects. It is done using SOBEL edge masks with IMFILTER. The fig. 14(b) shows the magnitude gradient of gray scale image.

Applying watershed transform without markers leads to over-segmentation, the next fig. 14(c) shows watershed transform applied directly to magnitude gradient image resulting into over-segmentation.

Now after using marker based approach (foreground and background markers by morphological operations) to identify the region of MAXIMA in marked objects the resultant image is obtained with foreground markers, background markers and boundaries. Now applying watershed transform to the modified segmentation function obtained in previous step and superimposing foreground markers, background markers, and segmented object boundaries on the original image, the resultant image fig. 14 (d) is obtained. It shows image with markers and boundaries superimposed on original image with watershed ridge line and surface area of segmented cement deposit.

Using watershed transform the segmented cement deposit is shown by SKYBLUE color image on red background with label matrix (LRGB) and is shown in fig. 14(e).

Watershed transform for bird deposits of thermal image: When marker based watershed algorithm is applied on thermal image fig. 12(b) of bird deposit, the result are

shown in fig. 15(a) to fig. 15(f).

(a) (b)

(c) (d)

(e) (f)

Fig. 15(a) Shows gray scale image of original thermal image, Fig. 15(b) gradient magnitude image, Fig. 15(c) over-segmentation of watershed transform, Fig. 15(d) markers and object boundaries super imposed on original thermal image, Fig. 15(e) colored segmented watershed transform label matrix, Fig. 15(f) superimposed segmented image of

watershed transform

The figures obtained for bird deposits are due to similar procedure described for cement deposit. The fig. 15(a) is the gray scale image obtained from RGB thermal image

The fig. 15(b) shows the magnitude gradient of gray scale image to detect the dark regions of desired segmented bird deposits. The fig. 15(c) shows over-segmented image when applying watershed transform to gradient magnitude image without markers.

Fig. 15(d) shows image with markers and boundaries superimposed on original image after using marker based approach through morphological operations.

The fig. 15(e) shows the watershed transform label matrix (LRGB) with pseudo-colors to display the segmented bird deposited area more clearly. The blue area shows the segmented image of bird deposit with red background. The green colored area is a segmented image of other small deposit highlighted with bird deposit.

The fig. 15(f) is the superimposed image obtained after transparently superimposing the obtained watershed transform label matrix (LRGB) on original image. This figure shows the segmented bird deposit on original image for better view.

VIII. R ESULTS A ND D ISCUSSIONS

The measurements show that as temperature of cement and bird deposits rise the power output of solar panels reduce. The temperature variation for ambient temperature and temperature of solar panel string containing deposits is also shown with the help of graphs. As the temperature of solar cell having deposits, temperature of solar panel string is also affected when these depositions occur on solar panel surface.

The image segmentation is used to find the region of interest. Thermal image of cement and bird deposits contain many small pellets of cement and other type of dirt, so image segmentation provides the desired region of cement and bird deposits from thermal image using marker based watershed transform. Finally the segmented image is superimposed over real image to get a real time view of the segmented cement and bird deposits. Therefore watershed transform is a good technique for image segmentation.

The above table 4 shown below represents the area, perimeter, major axis length and minor axis length, obtained from binary image of original thermal images for cement and bird depositions are calculated in pixels. These pixel concentration of segmented image show the intensity of hot regions for cement and bird depositions in segmented thermal image.

Table 4. Features of segmented image

IX.C ONCLUSION

This paper explains the effect of temperature on the performance of solar panels having cement and bird deposits on its surface. These deposits rise temperature of deposited area and produce heating, which result into loss of electrical power generation so thermal images captured by thermal imaging camera are utilized for analysis because these images captures the infrared radiations emitted by cement and bird deposits in heat form. The heat emitted by these deposits is not visible in normal camera photograph but is clearly visible thermal image in terms of temperature.

The first part of experimental work include analysis of cement and bird deposits through principle of thermal imaging. The normal camera photographs and thermal images of both deposits are compared along with electrical and thermal parameters to show the heat produced by these deposits. The electrical parameters along with temperature of cement and bird deposits and other parameters are shown in table 2 and table 3, indicating there is a decrease in generation of electrical power output of solar panels as temperature of deposits as well as temperature of other parameters increase. This variation of temperature due to these deposits with ambient temperature is shown in fig. 8 and fig. 11 with the help of temperature curves.

The second part of work deals with thermal image segmentation of cement and bird deposits for better visual analysis through MATLAB digital image processing. The desired cement and bird deposits are segmented from original thermal images through marker based watershed transform approach of image segmentation. The final segmented images of cement and bird deposits give colored images for better visualization. Therefore the work done presents a novel method of analyzing effect of cement and bird deposits by combining thermal imaging with image segmentation in order to better provide a better view of understanding.

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time Analyses for PV system monitoring byDigital Image Processing Techniques”, Int. Conf. Event-based Control, Communication, and Signal Processing (EBCCSP), IEEE, Krakow, pp. 1-6, 2015

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Segmentation of Natural Images”, Int. J. Image, Graphics and Signal Processing, MECS Press, vol. 1, pp. 28-34, 2012

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Authors’ Profiles

Akash Singh Chaudhary completed

his degree of B Sc Engineering

(Electrical) in 2005 and M.Tech.

(Engineering systems) in 2007 from

Faculty of Engineering, Dayalbagh

Educational Institute, Deemed

University, Agra, Uttar Pradesh, India.

He has total seven years of teaching experience as Guest Lecturer, Lecturer, Sr. Lecturer and Assistant Professor in Electrical Engineering Department of various institutions. He is actively involved in teaching and currently working as research scholar to pursue Ph.D. from Dayalbagh Educational Institute, Deemed University, Agra, Uttar Pradesh, India in the area of electrical power system with research objectives in solar photo-voltaic.

Mr. Chaudhary is a life member of ISTE and his research interest includes networks analysis, electrical machines, solar photo-voltaic and digital image processing.

D.K. Chaturvedi did his B.

E. from

Govt. Engineering College Ujjain, M.P.

then he did his M.Tech. (Gold medallist)

and Ph.D. from D.E.I. Dayalbagh. He

has received Young Scientists

Fellowship from DST, Government of

India. He is the Fellow IE(I), ASI and

IETE. ADRDE lab of DRDO conferred him life time achievement award for his valuable contributions in field of aeronautics. He is the consultant of DRDO. He had edited a book on ethics and values; and also authored three books in soft computing, modelling and simulation and electrical machines lab manual.

Presently Prof. Chaturvedi is working in Elect. Engineering. and having additional load of HOD, Department. of Footwear Technology, Training and Placement officer, D.E.I. and Adviser, IEI Students’ Chapter (Elect. Engineering.).

How to cite this paper: Akash Singh Chaudhary, D.K. Chaturvedi," Thermal Image Analysis and Segmentation to Study Temperature Effects of Cement and Bird Deposition on Surface of Solar Panels", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.9, No.12, pp.12-22, 2017.DOI: 10.5815/ijigsp.2017.12.02

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关于图像分割算法的研究

关于图像分割算法的研究 黄斌 (福州大学物理与信息工程学院 福州 350001) 摘要:图像分割是图像处理中的一个重要问题,也是一个经典难题。因此对于图像分割的研究在过去的四十多年里一直受到人们广泛的重视,也提山了数以千计的不同算法。虽然这些算法大都在不同程度上取得了一定的成功,但是图像分割问题还远远没有解决。本文从图像分割的定义、应用等研究背景入手,深入介绍了目前各种经典的图像分割算法,并在此基础比较了各种算法的优缺点,总结了当前图像分割技术中所面临的挑战,最后展望了其未来值得努力的研究方向。 关键词:图像分割 阀值分割 边缘分割 区域分割 一、 引言 图像分割是图像从处理到分析的转变关键,也是一种基本的计算机视觉技术。通过图像的分割、目标的分离、特征的提取和参数的测量将原始图像转化为更抽象更紧凑的形式,使得更高层的分析和理解成为可能,因此它被称为连接低级视觉和高级视觉的桥梁和纽带。所谓图像分割就是要将图像表示为物理上有意义的连通区域的集合,也就是根据目标与背景的先验知识,对图像中的目标、背景进行标记、定位,然后将目标从背景或其它伪目标中分离出来[1]。 图像分割可以形式化定义如下[2]:令有序集合表示图像区域(像素点集),H 表示为具有相同性质的谓词,图像分割是把I 分割成为n 个区域记为Ri ,i=1,2,…,n ,满足: (1) 1,,,,n i i j i R I R R i j i j ===??≠ (2) (),1,2,,i i i n H R True ?== (3) () ,,,i j i j i j H R R False ?≠= 条件(1)表明分割区域要覆盖整个图像且各区域互不重叠,条件(2)表明每个区域都具有相同性质,条件(3)表明相邻的两个区域性质相异不能合并成一个区域。 自上世纪70年代起,图像分割一直受到人们的高度重视,其应用领域非常广泛,几乎出现在有关图像处理的所有领域,并涉及各种类型的图像。主要表现在: 1)医学影像分析:通过图像分割将医学图像中的不同组织分成不同的区域,以便更好的

图像分割技术的GUI设计

图像分割技术的GUI设计 一、概述(意义及背景) 图像分割就是把图像分成若干个特定的、具有独特性质的区域并提出感兴趣目标的技术和过程。它是由图像处理到图像分析的关键步骤。现有的图像分割方法主要分以下几类:基于阈值的分割方法、基于区域的分割方法、基于边缘的分割方法以及基于特定理论的分割方法等。1998年以来,研究人员不断改进原有的图像分割方法并把其它学科的一些新理论和新方法用于图像分割,提出了不少新的分割方法。图像分割后提取出的目标可以用于图像语义识别,图像搜索等等领域。 二、设计方案 利用MATLAB中的GUI(图形用户界面),实现图像的读取,边缘检测,四叉树分解,直方图阈值分割,二值化差值的实现,并设计了退出按钮。 三、实现步骤 1、打开MATLAB; 2、打开Command Window 窗口中输入guide或点击快捷键 ; 3、在GUIDE Quick Start 窗口中选择Blank GUI(Default)中选择Blank GUI(Default),再单击OK; 4、在新出现的窗口中选择需要的GUI控件; 5、在控件上右击选择View Callbacks—callback; 6、输入各控件对应的回调函数; 四、系统调试及验证 完成后系统是这样的

1、单击系统前置图的运行按钮进入系统调试 2、点击第一个模块相应按钮完成相应实验 点击读取图片按钮的效果点击图像边缘检测按钮的效果 点击四叉树分解按钮的效果点击直方图阈值分割按钮的效果3、点击第二个模块相应按钮完成相应的实验

点击读取原图按钮的效果点击读取背景图按钮的效果 点击二值化差值图按钮的效果 4、点击退出按钮结束实验 点击退出按钮结束实验 五、参考文献 [1] 杨帆.数字信号处理与分析[M]. 北京:北京航空航天大学出版社,2010. [2] 徐飞,施晓红.MATLAB应用图像处理[M].西安.西安电子科技大学出版社,

图像分割方法综述

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图像分割方法的比较研究

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图像分割技术与MATLAB仿真

中南民族大学 毕业论文(设计) 学院: 计算机科学学院 专业: 自动化年级:2012 题目: 图像分割技术与MATLAB仿真 学生姓名: 高宇成学号:2012213353 指导教师姓名: 王黎职称: 讲师 2012年5月10日

中南民族大学本科毕业论文(设计)原创性声明 本人郑重声明:所呈交的论文是本人在导师的指导下独立进行研究所取得的研究成果。除了文中特别加以标注引用的内容外,本论文不包含任何其他个人或集体已经发表或撰写的成果作品。本人完全意识到本声明的法律后果由本人承担。 作者签名:年月日

目录 摘要 (1) Abstract (1) 引言 (3) 1 图像分割技术 (3) 1.1 图像工程与图像分割 (3) 1.2 图像分割的方法分类 (4) 2 图像分割技术算法综述 (5) 2.1 基于阈值的图像分割技术 (5) 2.2边缘检测法 (5) 2.3 区域分割法 (7) 2.4 基于水平集的分割方法 (8) 2.5 分割算法对比表格 (8) 3基于水平集的图像分割 (9) 3.1 水平集方法简介 (9) 3.2 水平集方法在图像分割上的应用 (9) 3.3 仿真算法介绍 (10) 3.4 实验仿真及其结果 (11) 结论 (18) 致谢 (19) 参考文献 (19)

图像分割技术研究及MATLAB仿真 摘要:作为一项热门的计算机科学技术,图像分割技术已经在我们生活中越来越普及。顾 名思义这项技术的目的就是,将目标图像从背景图像中分离出去。由于这些被分割的图像区域在某些属性上很相近,因此图像分割与模式识别以及图像压缩编码有着密不可分的关系。完成图像分割所采用的方法各式各样,所应用的原理也不同。但他们的最终目的都是把图像中性质相似的某些区域归为一类,把性质差异明显的不同区域分割开来。通常在分割完成之后,我们就要对某些特定区域进行分析、计算、评估等操作,因而分割质量的好坏直接影响到了下一步的图像处理[1],因此图像分割是图像处理的一个关键步奏。图像分割技术在各个领域都有着及其重要的意义;在工业上有卫星遥感,工业过程控制监测等等;在医学方面,水平集的分割方法还可以通过医学成像帮助医生识别模糊的病变区域;在模式识别领域还可应用到指纹扫描、手写识别、车牌号识别等等。 本课题的研究内容是对图像分割技术的几种常用的方法进行综述和比较,并基于其中一种方法进行MATLAB仿真测试,给出性能分析比较结果。 关键字:图像分割,MA TLAB仿真,模式识别 Image Segmentation and Matlab Simulation Abstract:Image segmentation is to image representation for the physically meaningful regional connectivity set, namely according to the prior knowledge of target and background, we on the image of target and background of labeling and localization, then separate the object from the background. Because these segmented image regions are very similar in some properties, image segmentation is often used for pattern recognition and image understanding and image compression and coding of two major categories. Because the generated in the segmented region is a kind of image content representation, it is the image of visual analysis and pattern recognition based and segmentation results of quality of image analysis, recognition and interpretation of quality has a direct impact. Image segmentation it is according to certain features of the image (such as gray level, spectrum, texture, etc.) to a complete picture of the image is segmented into several meaningful area. These features made in a certain region of consistent or similar, and between different regions showed significantly different. Image segmentation technology in various fields have most of the field and its important significance in digital image processing, image segmentation has a wide range of applications, such as industrial automation, process control, online product inspection, image coding, document image processing, remote sensing and medical image analysis, security surveillance, as well as military, sports and other aspects. In medical image processing and analysis, image segmentation for body occurrence of three-dimensional display of the diseased organ or lesion location determination and analysis plays an effective role in counseling; in the analysis and application of road traffic conditions,

彩色图像分割技术研究本科毕业论文

彩色图像分割技术研究本科毕业论文 目录 1. 引言 (1) 1.1.课题的研究背景和意义 (1) 1.2.彩色图像分割的现状 (2) 1.3.本文的容安排 (5) 2.彩色图像分割研究 (6) 2.1.数字图像处理概述 (6) 2.2.常用的颜色空间 (7) 2.3.彩色图像分割方法 (9) 2.3.1.阈值化方法 (10) 2.3.2.基于边缘的分割方法 (10) 2.3.3.基于区域的分割方法 (12) 3.无监督彩色图像分割 (13) 3.1.概述 (13) 3.2.颜色空间的转换 (14) 3.3.Sobel算子边缘提取 (15) 3.4.种子的选取 (16) 3.5.区域生长与合并 (17) 4.实验结果与分析 (18)

5.结论 (20) 参考文献 (21) 谢辞 (23)

1. 引言 1.1.课题的研究背景和意义 在人类所接收的信息中,有80%是来自视觉的图形信息,对获得的这些信息进行一定的加工处理也是目前一种广泛的需求,图像分割就是将图像中感兴趣的部分分割出来的技术。在图像分割的基础上,才能对目标进行特征提取和参数测量,使得更高层的图像分析和理解成为可能。因此,对图像分割的研究在图像处理领域具有非常重要的意义。 图像分割作为图像分析的基础,是图像分析过程中的关键步骤。图像分割,顾名思义是将图像按照一定的方法划分成不同的区域,使得同一区域像素之间具有一致性,不同区域间不具有这种一致性。 因为人眼对亮度具有适应性,即在一幅复杂图像的任何一点上只能识别几十种灰度级,但可以识别成千上万种颜色,所以许多情况下,单纯利用灰度信息无法从背景中提取出目标,还必须借助于色彩信息。由于彩色图像提供了比灰度图像更加丰富多彩的信息,因此随着计算机处理能力的提高,彩色图像处理正受到人们越来越多的关注。 自数字图像处理问世不久就开始了图像分割的研究,吸引了很多研究者为之付出了巨大的努力,在不同的领域也取得了很大的进展和成就,现在人们还一直在努力发展新的、更有潜力的算法,希望实现更通用、更完美的分割结果。目前,针对各种具体问题已经提出了许多不同的图像分割算法,对图像分割的效果也有很好的分析结论。但是,由于图像分割问题所面向领域的特殊性,而且问题本身具有一定的难度和复杂性,到目前为止还不存在一个通用的分割方法,也不存在一个判断分割是否成功

图像分割技术

图像分割技术 图像分割就是将一副数字图像分割成不同的区域,在同一区域内具有在一定的准则下可认为是相同的性质,如灰度、颜色、纹理等,而任何相邻区域之间器性质具有明显的区别。 主要包括:边缘分割技术、阈值分割技术和区域分割技术。 1.边缘分割技术 边缘检测是检测图像特性发生变化的位置,是利用物体和背景在某种图像特性上的差异来实现的。不同的图像灰度不同,边界处会有明显的边缘,利用此特征可以分割图像。边缘检测分割法是通过检测出不同区域边界来进行分割的。 常见的边缘检测方法:微分算子、Canny算子和LOG算子等,常用的微分算子有Sobel算子、Roberts算子和Prewit算子等。 (1)图像中的线段 对于图像的间断点,常用检测模板: -1 -1 -1 -1 8 -1 -1 -1 -1?????????? 对于图像中的线段,常用的检测模板: 检测图像中的线段: close all;clear all;clc; I=imread('gantrycrane.png'); I=rgb2gray(I); h1=[-1,-1,-1;2 2 2;-1 -1 -1];%模板 h2=[-1 -1 2;-1 2 -1;2 -1 -1]; h3=[-1 2 -1;-1 2 -1;-1 2 -1]; h4=[2 -1 -1;-1 2 -1;-1 -1 2]; J1=imfilter(I,h1);%线段检测 J2=imfilter(I,h2); J3=imfilter(I,h3); J4=imfilter(I,h4); J=J1+J2+J3+J4;%4种线段相加 figure, subplot(121),imshow(I); subplot(122),imshow(J); (2)微分算子 ○1Roberts算子的计算公式: 采用edge()函数进行图像的边缘检测。 Roberts算子进行图像的边缘检测: close all; clear all;clc; I=imread('rice.png'); I=im2double(I); %Roberts算法进行边缘检测

第七章 图像分割

第七章图像分割 1.什么是区域?什么是图像分割? 区域是指相互连通的、有一致属性的像素的集合。 图像分割是指把图像分成互不重叠的区域并提出感兴趣目标的技术。 2.边缘检测的理论依据是什么?有哪些方法?各有什么特点? 边缘能勾画出目标物体轮廓,使贯彻着一目了然,包含了丰富的信息(如方向、阶跃性质、形状等),是图像识别中抽取的重要属性。 (1)梯度算子。特点:仅计算相邻像素的灰度差,对噪声敏感,无法抑制噪声的影响。 (2)Roberts梯度算子。特点:与梯度算子检测边缘的方法类似,但效果较梯度算子略好。 (3)Prewitt和Sobel算子。特点:不仅能检测边缘点,且能进一步抑制噪声的影响,但检测的边缘较宽。 (4)方向算子。特点:边缘检测能力强,且抗噪性能好。 (5)拉拉普拉斯算子。特点:各向同性、线性和位移不变的;对细线和孤立点检测效果好。但边缘方向信息丢失,常产生双像素的边缘,对噪声有双倍加强效果。 (6)马尔算子。特点: (7)Canny边缘检测算子。特点:可以减小检测中的边缘中断,有利于得到较为完整的线段。 (8)沈俊边缘检测方法。特点:用对称的指数函数滤波器进行平滑,并在阶跃边缘,可加白噪声的模型下,按信噪比最大准则,证明了对称的指数函数滤波器是最 佳滤波器。 (9)曲面拟合法。特点:对一些噪声比较严重的图像进行边缘检测可以取得较为满意的结果。 3.拉普拉斯边缘检测算子与拉普拉斯边缘增强算子有何区别? 拉普拉斯边缘检测算子模板中心是-4,拉普拉斯边缘增强算子模板中心是+5。 4.什么是Hough变换?Hough变换检测直线时,为什么不采用y=kx+b的表达形式?试 述采用Hough变换检测直线的原理。 直角坐标系中的一条直线对应极坐标系中的一点,这种线到点的变换就是Hough变换。在直角坐标系中过任一点(x0,y0)的直线系,满足 其中而这些直线 在极坐标系中所对应的点(ρ、θ)构成一条正弦曲线。反之在极坐标系中位于这条正弦曲线上的点,对应直角坐标系中过点(x0,y0)的一条直线,设平面上有若干点,过每点的直线分别对应于极坐标系上的一条正弦曲线。若这些正弦曲线有共同的交点(ρ‘、θ’),则 这些点共线,且对应的直线方程为 5.常用的三种最简单图像分割法各有何特点?

图像分割技术的研究背景及意义

图像分割技术的研究背景及意义 1概述 2图像分割技术的研究背景及意义 2.1阈值分割方法 2.2基于边缘的分割方法 2.3基于区域的分割方法 2.4 结合特定理论工具的分割方法 1概述 图像的研究和应用中,人们往往对图像中的某些部分感兴趣,这些感兴趣的部分一般对应图像中特定的、具有特殊性质的区域(可以对应单一区域,也可以对应多个区域),称之为目标或前景;而其他部分称为图像的背景。为了辨识和分析目标,需要把目标从一幅图像中孤立出来,这就是图像分割要研究的问题。 2图像分割技术的研究背景及意义 图像分割是图像处理中的一项关键技术,也是一经典难题,发展至今仍没有找到一个通用的方法,也没有制定出判断分割算法好坏的标准,对近几年来出现的图像分割方法作了较为全面的综述,探讨了图像分割技术的发展方向,对从事图像处理研究的科研人员具有一定的启发作用。 图像分割是图像分析的第一步,图像分割接下来的任务,如特征提取、目标识别等的好坏,都取决于图像分割的质量如何。由于该课题的难度和深度,进展比较缓慢。图像分割技术自20世纪70年代起一直受到人们的高度重视,虽然研究人员针对各种问题提出了许多方法,但迄今为止仍然不存在一个普遍适用的理论和方法。另外,还没有制定出选择适用分割算法的标准,这给图像分割技术的应用带来许多实际问题。最近几年又出现了许多新思路、新方法或改进算法,对一些经典方法和新出现的方法作了概述,并将图像分割方法分为阈值分割方法、边缘检测方法、区域提取方法和结合特定理论工具的分割方法4类。

2.1阈值分割方法 阈值分割方法的历史可追溯到近40前,现已提出了大量算法。阈值分割法就是简单的用一个或几个阈值将图像的直方图分成几类,图象中灰度值在同一个灰度类内的像素属于同一个类。它是一种PR法。其过程是决定一个灰度值,用以区分不同的类,这个灰度值就叫阈值。它可以分为全局阈值分割和局部阈值分割。所谓全局阈值分割是利用整幅图像的信息来得到分割用的阈值,并根据该阈值对整幅图像进行分割;而局部阈值分割是根据图像中的不同区域获得对应的不同区域的阈值,利用这些阈值对各个区域进行分割,即一个阈值对应一个相应的子区域,这种方法也叫称为适应阈值分割。可以看出,确定一个最优阈值是分割的关键。现有的大部分算法都是集中在阈值确定的研究上。阈值分割方法根据分割算法所有的特征或准则,还可以分为直方图与直方图变换法、最大类空间方差法、最小误差法与均匀化误差法、共生矩阵法、最大熵法、简单统计法与局部特性法、概率松驰法、模糊集法、特征空间聚类法、基于过渡区的阈值选取法等。 目前提出了许多新方法,如严学强等人提出了基于量化直方图的最大熵阈值处理算法,将直方图量化后采用最大熵阈值处理算法,使计算量大大减小。薛景浩、章毓晋等人提出基于最大类间后验交叉熵的阈值化分割算法,从目标和背景的类间差异性出发,利用贝叶斯公式估计象素属于目标和背景两类区域的后验概率,再搜索这两类区域后验概率之间的最大交叉熵。这种方法结合了基于最小交叉熵以及基于传统香农熵的阈值化算法的特点和分割性能,取得很好的通用性和有效性,该算法也容易实现二维推广,即采用二维统计量(如散射图或共生矩阵)取代直方图,以提高分割的准确性。俞勇等人提出的基于最小能量的图像分割方法,运用了能量直方图来选取分割阈值。任明武等人提出的一种基于边缘模式的直方图构造新方法,使分割阈值受噪声和边缘的影响减少到最小。程杰提出的一种基于直方图的分割方法,该方法对Ostu准则的内在缺陷进行了改进,并运用对直方图的预处理及轮廓追踪,找出了最佳分割阈值。此方法对红外图像有很强的针对性,付忠良提出的基于图像差距度量的阈值选取方法,多次导出Ostu方法,得到了几种与Ostu类似的简单计算公式,使该方法特别适合需自动产生阈值的实时图像分析系统。陈向东、常文森等人提出了基于小波变换的图像分数维计算方法,利用小波变换计算图像的分数维准确性高的特性。结果表明计算出的图像分数维准确,而且通过应用快速小波变换可以满足实时计算的要求,为实时场景分析提供有效的方法。建立在积分几何和随机集论基础之上的数学形态学以其一整套变换、概念和算法为数学工具,提供了并行的、具有鲁棒性的图像分割技述。它不仅能得到图像中各种几何参数的间接测量,反映图像的体视特性,而

图像分割阈值选取技术综述

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