Computer-aided visual inspection

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2015 IEEE 12th International Conference on Electronic Measurement & Instruments ICEMI’2015 
Computer-aided visual inspection 
Cristian Tomazela Prado1; Felipe de Deus; Carlos Henrique Contieri; Mônica Ronobo Coutinho; 
Wagner André dos Santos Conceição; Flávio Clareth Colman; Cid Marcos Gonçalves Andrade2 
1Department of Mechanical Engineering, UniversidadeEstadual de Maringá 
Avenida Colombo, 5790, Maringá – PR, Brazil 
Email: cristiantomazela_prado@hotmail.com 
2Department of Chemical Engineering, UniversidadeEstadual de Maringá 
Avenida Colombo, 5790, Maringá – PR, Brazil 
Email: cidmga@yahoo.com.br 
Abstract –The quality control in industrial processes and 
production lines is increasingly rigorous due to governmental 
issues and the competition among other industries. These make 
new studies in this field even more important, especially on 
defects analysis and visual inspection. Among the methods of 
inspections commonly used the highlighted ones include the 
direct inspection that is performed without the aid of optical 
devices, and the remote inspection that makes use of optical 
instruments, both having an inspector responsible for a 
subjective evaluation. Such methods are usually the first used in 
quality control of products. In this paper we propose a remote 
and automatic computational image analysis to verify the 
presence of defects in products in a production line. 
 
Keywords – quality control, image acquisition, visual inspection. 
I. INTRODUCTION 
Systems of inspection that make use of some artificial 
vision resource, process controllers and computers have 
become increasingly used in the industry without the 
inconveniences of human’s vision [5]. The author also 
mentions that the implementation of those systems 
influence directly in the organization behavior, because 
defects that once were not spotted are now detected. 
According to [8], visual inspection is adaptable to 
innumerous situations. In his work, the author made use of 
computational analysis technics to inspect explosion, 
traction and pressure tests in flexible tubes. The results 
were satisfactory, however the method used could not find 
imperfections or even to make comparisons with 
standardized parts. 
In [1], a work developed to speed up the classification 
process of two different wood blades to improve the wood 
separation system of the company studied. This study 
showed a creative idea of utilizing computational analysis 
to sort objects so that future works could go even deeper in 
applications such as classifying the quality of the product. 
A quality control system of chicken eggs in production 
lines were developedin [3]. Image analysis and processing 
were used to detect specific defects that are usually 
external - such as cracks, dirt, blood spots, and yolk 
leakage – and separate the products based on the result 
obtained. 
 
 
Based on the previous works, an image study had to be 
conducted to develop this work. A digital image is 
considered as a discrete representation of data that have 
spatial (layout) and intensity (color). The elements that 
compose an image are known as pixels, an abbreviation for 
picture element. A two-dimensional image represents the 
response of some sensor to a series of fixed positions (m = 
1, 2, ..., M; n = 1, 2, ..., N) in two-dimensional Cartesians 
coordinates that come from a 2-D continuous spatial signal 
I(x,y) by a process known as discretization [2]. On Figure 
1, this image discrete representation is shown. 
 
Fig.1. Spatial information of a pixel in an image. 
In [2], the authors emphasize that all colors can be 
represented in a 3-D space built in RGB (R – red; G – 
green; B – blue) format, with each axis having an intensity 
value between 0 and 1, so that (0,0,0) represents black and 
(1,1,1) white. 
Given the abovementioned improvements on the 
quality control using computational aid and also the basics 
of image’s concept, this study aimed to develop a system 
of defects’ control that identify them automatically in a 
huge variety of products (parts, foods, machines, and so on) 
taking the ones with visual defects out of the production 
line. For that, concepts of engineering such as 
electro-pneumatic systems and algorithms development 
were widely used in this work. 
This paper will be structured in the next three sections 
as follows: In Section II we will show the main goals of 
this work and summarize how it was performed; In Section 
III we will expose the materials and methods used; In 
Section IV we will present an example performed to better 
explain the analysis and the main results obtained in the 
actual experiment. 
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978-1-4799-7071-1 /15/$31.00 ©2015 IEEE 
 
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2015 IEEE 12th International Conference on Electronic Measurement & Instruments ICEMI’2015 
II. PROBLEM DEFINITION 
The objective of this work is to develop a computational 
image analysis system that operates automatically into 
products on the production line, removing the defective 
ones from the remaining process by an electro-pneumatic 
system activated by a MATLAB algorithm and its Image 
Processing toolbox that compares the products in the 
process line with standardized images of that product. 
The above-mentioned image analysis takes into account 
a pre-determined minimum defect size established by the 
responsible for the analysis and can be changed in the 
algorithm for different types of products and defects being 
analyzed. 
It aims to reduce the need for skilled labor to perform 
those inspections, and to increase its speed and efficiency. 
This way, the subjectivity of a human analysis would be 
replaced by a more reliable and probably less expensive 
computational system. 
III. MATERIALS AND METHODS 
The first step was to build a conveyor moved by an 
electric motor coupled with a frequency inverter to 
simulate a production line. Then, a electro-pneumatic 
system integrated to an image acquisition set-up. These are 
explained in sub-sections A and B. 
 To make sure the developed system works, two 
paper-boxes filled with juice (Figure 2) were used. In one 
of them, there were no defects and it was considered as the 
standard object. The second one had a black mark in its 
side, simulating a visual defect. Thus, the system should 
take the second box out of the conveyor, and the standard 
box should keep moving with the conveyor. 
 
Fig. 2. Juice box used in the defect analysis. 
A. Image acquisition 
The code responsible for making the analysis on the 
desired object makes use of Data Acquisition, Image 
Acquisition and Image Processing toolboxes available on 
MATLAB® version 8.0.0.783 64-bits. The algorithm 
needs a standard image, which is considered to have no 
defects, which in this case is the one on Figure 2. 
The presence sensor used in this work is the E18 
D80NK-N.Its low price, fairly easy installation and easy to 
set the range of the measurement were the reasons to use 
this sensor. About how it works, when the infrared emitted 
by the sensor reaches some obstacle in its range of 
measurement, the difference of potential between its 
terminals becomes negative, and while there is nothing 
being reached by the infrared this difference of potential 
stays positive. This sensor needs a 5V source, and to obtain 
that, a plug-and-play board from a computer cooler was 
used. 
The DAQ device used is NI-USB 6008. It was 
responsible to interconnect the presence sensor and the 
integrated camera, to make possible to capture the images 
to be analyzed. It was also responsible for the activation of 
the electro-pneumatic circuit due to its connection with the 
MATLAB® code, since the DAQ device may be used for 
analog output, being used as a power source of either 2.5 or 
5 V. 
B. Electro-pneumatic design 
The electro-pneumatic circuit was designed to take the 
defected object out of production line, whichin this work 
was simulated by a conveyor with a PVC belt.The circuit 
was designed on FESTO FluidSim®. 
 
Fig.3. Electro-pneumatic circuit 
Table 1 gives a brief description of each element of the 
electro-pneumatic design (Figure 2). 
Table 1. Description of the electro-pneumatic circuit elements. 
ELEMENT DESCRIPTION 
A e B Pressurized-air distribution center 
C Solenoid valve 5-2 ways with spring return
D Double-action piston 
F Trigger button 
G Relay 
H Key trigged by the relay 
I Solenoid 
It is worth to mention that the element F (trigger button) 
is not really a button. Actually, the before-mentioned 
circuit is triggered by MATLAB®, which is connected to 
an electronic circuit commanded by the DAQ device. The 
latter circuit is shown in figure 3 below. 
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Fig.4. Electronic circuit. 
IV. RESULTS AND DISCUSSION 
A. EXAMPLE 
As mentioned before, the algorithm needs a standard 
image in order to compare with the object that will be 
analyzed. To better explain this, a couple of CAD images 
were analyzed with the algorithm. The first image 
represents the standard one, the other has a few black spots 
on it, to represent visual defects. 
 
 
Fig.5. Standard (left) image and image with visual defects (right) 
The first action of the developed algorithm is to make a 
pixel subtraction from one image to the other, resulting in a 
new image. Afterwards, the algorithm enhance the pixels 
brightness (Figure 12), and this is very important because 
it highlight the differences between the images, easing the 
next step of the algorithm. 
 
Fig. 6. Images resulted from subtraction (top) and brightness 
enhancement (bottom). 
The next step in the program is converting the previous 
image (RGB) to a binary (black and white) image (Figure 
7). This conversion is important because some functions in 
the code work with binary images only, which are 2-D 
vectors. The pixels have only two possible values, one for 
black and the other for white. 
 
Fig.7. Binary image. 
Finally, the code calculate the number of continuous 
white areas in the binary image, which are the defects, 
indicating its area in square pixels, being 72 pixels equal to 
1 inch. This way, it is possible to control the minimum area 
considered as a defect. Figure 8 shows the result obtained 
with the code using the example image. 
Besides the aforementioned algorithm, a user interface 
was created. This interface do not need any version of 
MATLAB® installed, it only needs a MATHWORKS© 
plugin, which is free and can execute all the Toolboxes 
functions. 
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2015 IEEE 12th International Conference on Electronic Measurement & Instruments ICEMI’2015 
 
Fig. 8. Result showing the number of defects and their areas in 
square pixels. 
A picture shows all the systems interconnected, 
exhibiting how the different designed circuits are 
connected to allow the defect analysis of the juice 
paper-box. 
 
Fig. 9. Picture showing all the circuits and the juice paper-box. 
B. ACTUAL TEST 
In order to perform the experiment, the first challenge 
was to find a working frequency of the motor. The box 
should have a low velocity, low enough to be caught by the 
presence sensor to send a signal to the camera and it could 
get an image with a decent quality so that it would be 
possible to perform an image analysis. Thus, the minimum 
value possible (1.5 Hz) with the frequency inverter used 
was chosen. 
With the aforementioned parameters set, it was 
possible to run the experiment. First, the box with no 
defects (standard image) was put in the conveyor, and the 
piston was not acted by the electronic systems. Afterwards, 
the box with a black spot on it (defected) performed the 
experiment. This time, the algorithm detected the black 
spot and sent a signal to the electronic and 
electro-pneumatic systems acting the piston and taking it 
out of the conveyor. 
Figure 16 shows the result of the image analysis, 
showing that the defect was caught by the algorithm and 
highlighting its position and area. That was a very 
satisfactory result for either academic or industrial use. 
 
ACKNOWLEDGMENT 
The author wishes to thank the IEEE for providing this 
template and all colleagues who previously provided 
technical support. 
REFERENCES 
[1] C.Dell’Agnolo, “Controle da qualidade em indústria madeireira- 
uma introdução”, Universidade Federal de Santa Catarina (UFSC), 
Florianópolis, 2001. 
[2] Solomon and T. Breckon, “Fundamentals of Digital Image 
Processing”, Wiley-Blackwell: Oxford, UK – 2011. 
[3] S. Machado, “Sistema de Inspeção Visual Automática Aplicado ao 
Controle de Qualidade de Ovos em Linhas de Produção”, Centro 
Federal de Educa.ção Tecnológica de Minas Gerais (CEFET – 
MG), Belo Horizonte, 2009. 
[4] C. Aildefonso, “GESTÃO DA QUALIDADE”, Centro Federal de 
Educação Tecnológica do Espírito Santo (CEFET-ES), Vitória, 
2006. 
[5] N. Fialho, “A Aplicação dos Sistemas de Inspeção Visual na 
Indústria, Unidigital - Eletrônica e Sistemas de Automação”, Porto 
Alegre, 2013. 
[6] National Instruments do Brasil. Available on: http://brasil.ni.com, 
access in: 20/04/2014. 
[7] R. Sampaio, “Curso de Inspeção de Equipamentos – Inspeção 
Visual”, Rio de Janeiro, 2010.V. Montenegro, “Visão 
computacional aplicada à análise de deformações em ensaios de 
tubos flexíveis”, Universidade Federal Fluminense (UFF), Niterói, 
2012. 
AUTHOR BIOGRAPHIES 
 Cristian Tomazela Prado.He will receive Bachelors of 
Engineering from Unversidade Estadual de Maringá,Brazil，in 
2016.Now he is a Mechanical Engineering studentinthe 
Mechanical Engineering Department at Unversidade Estadual de 
Maringá，Brazil.His research interests include （data acquisition, 
image acquisition, control systems, and other areas related to 
Mechanical Engineering. 
 
 Felipe de Deus. He received Bachelorsof Mechanical 
Engineering fromUnversidade Estadual de Maringá, Brazil，in 
2015.His research interests include （data acquisition, image 
acquisition, control systems, and other areas related to 
Mechanical Engineering. 
 
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2015 IEEE 12th International Conference on Electronic Measurement & Instruments ICEMI’2015 
 Carlos Henrique Contier. He received Bachelorsof 
Mechanical Engineering fromUnversidade Estadual de Maringá, 
Brazil，in 2015.His research interests include data acquisition, 
image acquisition, control systems, and other areas related to 
Mechanical Engineering. 
 
 Mônica Ronobo Coutinho. He receivedMS from 
Universidade Federal de Santa Catarina,Brazil， in 2002 and 
PhD from Universidade Estadual de Maringá,Brazil ， in 
2006.Now he is a professorin Departamento de Engenharia de 
Alimentos / Universidade Estadual do Centro-Oeste,Brazil.His 
research interests include data food science and technology, 
acquisition, image acquisition, thermodynamic, dryer, operations 
separation, simulation, control systems, and other areas related to 
Engineering. 
 
 Wagner André dos Santos Conceição. He received MS 
from Universidade Federal de Santa Catarina,Brazil，in 2002 
and PhD from Universidade Estadual de Maringá,Brazil， in 
2007.Now he is a professorin Departamento de Engenharia 
Mecânica / Universidade Estadual de Maringá,Brazil. His 
research interests include data acquisition, image acquisition, 
simulation process, control systems, and other areas related to 
Engineering. 
 
 Flávio Clareth Colman. He received MS from 
Universidade Estadual de Maringá,Brazil，in 2012.Now he is a 
PhD candidate inDepartamento de Engenharia Química / 
Universidade Estadual de Maringá,Brazil.Now he is a professorin 
Departamento de Engenharia Mecânica / Universidade Estadual 
de Maringá,Brazil. His research interests include data acquisition, 
image acquisition, control systems, simulation open source, dryer, 
operationsseparationand other areas related to Engineering. 
 
 Cid Marcos G. Andrade.He received MS from 
Universidade de São Paulo,Brazil， in 1990 and PhD from 
Universidade Estadual de Campinas,Brazil，in 2000.Now he is a 
professorin Departamento de Engenharia Química / Universidade 
Estadual de Maringá,Brazil.His research interests include food 
science and technology, process control electronics, feedback, 
operations separation and mixing, dynamical systems, energy 
advantage, electronic systems measure and control. 
 
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