Machine Vision Project Report

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School of Electrical &ElectronicEngineering

ACADEMIC SESSION 2013/2014

EEE 355 Robotics and Machine Vision

Mini project

PCB Defects detection

Name : VINCENT KOK CHIN KEONG ([email protected])

MATRIC NO. : 112195

Date of submission : 3rd

june 2014

SUPERVISED BY: Dr. khoo bee ee



Table of contents

Chapter Description Page

1. 1.0 Abstract1

2. 2.0 Introduction2

3. 3.0 Methodology

A. Inspection Flow Chart

B. Image Subtraction Operation

C. OpenCV function used 3 - 5

4. 4.0 Results and Discussion6 - 10

5. 5.0 Limitation11

6. 6.0 Future Improvements11

7. 7.0 Conclusion11

8. 8.0 References 11

9. 9.0 Appendix12 - 13



EEE 355 Robotics & Machine Vision

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1.0 Abstract

The main objective of this project is to let students apply the image processing techniques

that been taught in class in a given conditions based on their title. This particular project

involved the detection of the defect exist in a PCB board. A Printed Circuit Board (PCB) is a

circuit board consists of electronics components mounted on the surface. Basically, to

produce a perfect bare PCB board, inspection of PCB is necessary to reduce defects. We

apply the machine vision concept to inspect the bare PCB. We first compare a standard PCB

image with a PCB image to be inspected using a simple subtraction algorithm that can

detect the defected region. Our focus is to detect the defect on the PCB, such as broken

track. There are so many algorithms out there to detect the defect on the PCB board.




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2.0 Introduction

Nowadays, PCB manufacturing is getting more and more important for the electronics

industry such as mobile phones, tablets, washing machines and prototyping boards. Hence,

it is very important to have machine vision inspection of the PCB to improve the quality of

the PCBs. As you know, in manufacturing industry, there are errors such as defects,

misalignment and orientation error. Hence we need automated inspection to analysed for

the errors. There are plenty of such algorithms out there now.

There are 3 main processes in inspection of PCBs : defect detection, defect classification,

and the location of the defect.

Of course, automated inspection system is better than human inspection in which

subjectivity, fatigue, slowness and high cost is involved. The technology is moving very fast

and hence a fast and efficient inspection system is required to make sure 100% quality PCBs

are produced. These conclude that automated systems are preferred in manufacturing

industry nowadays for higher productivity.

In my case study, I am going to investigate about how to detect a broken track on the PCB.

This project required us to only use the images captured by the laboratory camera or our

own digital camera devices. In this project, Visual Studio C++ 2010 was used as the main

compiler with linked libraries to the OpenCV image processing library.




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3.0 Methodology

A. Inspection Flow Chart

Load the image fromcomputer

Convert the images

to grayscale image

Threshold the image

Convert to binary

image

XOR operation on

the images

Resultant image

where defect

detected




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B. Image Subtraction Operation

Well, this is the simplest algorithm that we can find to detect the

difference between the two images, i.e. to detect the defect on the PCB. In other word,

image subtraction is performed in order to get the difference between two images. Inthis algorithm, we need to use two images – reference image and the inspected image.

In this method, we compare both the images pixel-by-pixel using XOR logic

operator. After performing the operation, we will get the resulting image that consist of

the defects. The subtract operation produces either negative or positive pixel value.

Therefore the outcome of this operation is divided into negative image and positive

image.

Pixel [Image 1] Pixel [Image 2] Pixel [Output

Image]0 0 0

0 1 1

1 0 1

1 1 0

The overview of the XOR/Subtraction operation process is shown below :

To perform the image subtraction operation, it is required that both images has same size in

terms of pixels. The logical XOR operation will show us the defect in inspected image as

compared with reference image




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C. OpenCV Functions Used

OpenCV function Explanation

Mat im_gray; Define the image matrix for “im_gray”

Mat im_rgb = imread( "PCB_no_defect.tif" ); Load the image “PCB_no_defect.tif” from

the disk and read it as RGB image.

cvtColor(im_rgb,im_gray,CV_RGB2GRAY); Convert the RGB image read to grayscale

image.

Mat result = img_bw_1^img_bw; Define the image matrix for result. The result

is obtained by XOR the reference image with

defected image.

namedWindow( "Display window1" ,

WINDOW_AUTOSIZE );

Create a window for display. The name of

the window is “Display window1”. The

window size is made to adjust automaticallyaccording to the size of image.

imshow( "Display window1" , result); Show the image “result” at “Display

window1”

imwrite( "Defect.jpg" ,result); Write the image “result” to the project

folder.




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4.0 Results And Discussion

A) Top Lighting

RGB Image ( Reference image ) RGB Image ( Defected image )

Grayscale Image (Reference Image ) Grayscale Image (Defected Image )




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Binary Image (Reference Image ) Binary Image (Defected Image )

Resultant image ( Defect detected)




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B) Top and 2 Sides Lighting

RGB Image ( Reference image ) RGB Image ( Defected image )

Grayscale Image (Reference Image ) Grayscale Image (Defected Image )




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Binary Image (Reference Image ) Binary Image (Defected Image)

Resultant image ( Defect detected)




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As you can see from the result, we first load the RGB image, both reference and defected

image. Next we will then grayscale both image. The reason for differentiating such images

from any other sort of colour image is that less information needs to be provided for each

pixel. In fact a `gray' colour is one in which the red, green and blue components all have

equal intensity in RGB space, and so it is only necessary to specify a single intensity value foreach pixel, as opposed to the three intensities needed to specify each pixel in a full colour

image.

Next, we convert the grayscale image to binary image that consist only binary pixel ‘1’ or ‘0’

for easy operation process later on.

Now, after we get both the binary image, we perform the XOR operation to detect the

defected region on the image.

If there is no defect detected, the window will show as below :

No defects detected !

Comparison between two different lighting

As you can see from the result of two different lighting A) Top Lighting and B) Top and 2

Sides Lighting, the result is kind deviate a little under different lighting with constant

threshold value of 138. The result for B) are subjected to more noise. However, both the

type of lighting are able to detect the defect and show it at the final result.

http://homepages.inf.ed.ac.uk/rbf/HIPR2/rgb.htm

http://homepages.inf.ed.ac.uk/rbf/HIPR2/colimage.htm




http://homepages.inf.ed.ac.uk/rbf/HIPR2/rgb.htm




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5.0 Limitations

This image subtraction algorithm actually has a lot of disadvantages and limitations :

It only can detect for the broken track but cannot detect like short circuit, missing

components.

The images size must be the same (i.e. the reference image and defected image

must have same size)

Both the inspected image and reference image must have the same orientation.

6.0 Further Improvement

This project can be further improved by upgrading defect detection algorithm to suit a

variety of lighting conditions. Besides, we could improve the algorithm to detect different

kind of defects such as missing components or short circuits. Improvement can be made on

stating the location for the defect and calculate the size of the defect.

7.0 Conclusion

In conclusion, we manage to complete the project with a great success in testing and

coding for the defect detection on the bare PCB. Indeed, throughout this project, we have

learnt how to use the OpenCV library in Visual Studio C++ 2010. We were also exposed to

the effects of different kind of lighting condition which could affect the performance of the

algorithm.

This project enable us to apply the knowledge and theory that we learnt in class to solve

real life problems.

8.0 Reference

1. Convert RGB to Black & White in OpenCV

- http://stackoverflow.com/questions/1585535/convert-rgb-to-black-

white-in-opencv 2. Load, Display and Save Image

- http://opencvexamples.blogspot.com/2013/09/opencv-example-to-

load-and-display-image.html#.U4lu8vmSz2P

3. Detection of Bare PCB Defects by Image Subtraction Method using

Machine Vision

- http://www.iaeng.org/publication/WCE2011/WCE2011_pp1597-

1601.pdf

http://stackoverflow.com/questions/1585535/convert-rgb-to-black-white-in-opencv





http://opencvexamples.blogspot.com/2013/09/opencv-example-to-load-and-display-image.html#.U4lu8vmSz2P





http://www.iaeng.org/publication/WCE2011/WCE2011_pp1597-1601.pdf














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9.0 Appendix

#include<opencv2\opencv.hpp>

using namespace cv;

int main ( int argc, char **argv )

{

Mat dst;

Mat im_gray;

Mat im_gray_1;

//Mat im_gray = imread("PCB_no_defect.tif",CV_LOAD_IMAGE_GRAYSCALE);

Mat im_rgb = imread("PCB_no_defect.tif");

//Mat im_gray_1 = imread("PCB_defect.tif",CV_LOAD_IMAGE_GRAYSCALE);

Mat im_rgb_1 = imread("PCB_defect.tif");

cvtColor(im_rgb,im_gray,CV_RGB2GRAY);

cvtColor(im_rgb_1,im_gray_1,CV_RGB2GRAY);

equalizeHist( im_gray, dst );

Mat img_bw = im_gray > 138;

Mat img_bw_1 = im_gray_1 > 138;

Mat result = img_bw_1-img_bw;

namedWindow( "Display window1", WINDOW_AUTOSIZE );// Create a window

for display.

imshow( "Display window1", result); // Show

namedWindow( "Display window2", WINDOW_AUTOSIZE );// Create a window

for display.

imshow( "Display window2",img_bw ); // Show our image

inside it.

namedWindow( "Display window3", WINDOW_AUTOSIZE );// Create a window for

display.




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imshow( "Display window3", img_bw_1);

imwrite("im_gray.jpg", im_gray);

imwrite("im_gray_1.jpg", im_gray_1);

imwrite("im_bw.jpg", img_bw);

imwrite("im_bw_1.jpg", img_bw_1);

imwrite("Defect.jpg",result);

waitKey(0); // Wait for a

keystroke in the window

return 0;

return 0;}

Machine Vision Project Report

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