APPLICATIONS OF MACHINE VISION

27
Applications of Machine Vision
Remigiusz Labudzki, Stanislaw Legutko
Abstract
Machine vision (system vision) it's a apply computer vision in industry. While computer vision is focused mainly on
image processing at the level of hardware, machine vision most often requires the use of additional hardware I/O
(input/output) and computer networks to transmit information generated by the other process components, such as a robot
arm. Machine vision is a subcategory of engineering machinery, dealing with issues of information technology, optics,
mechanics and industrial automation. One of the most common applications of machine vision is inspection of the products
such as microprocessors, cars, food and pharmaceuticals. Machine vision systems are used increasingly to solve problems of
industrial inspection, allowing for complete automation of the inspection process and to increase its accuracy and efficiency.
As is the case for inspection of products on the production line, made by people, so in case of application for that purpose
machine vision systems are used digital cameras, smart cameras and image processing software. This paper presents the
possible applications of machine vision in the present.
Keywords: machine vision, image processing, inspection
1 Introduction
The introduction of the automation has revolutionized
the manufacturing in which complex operations have been
broken down into simple step-by-step instruction that can be
repeated by a machine. In such a mechanism, the need for
the systematic assembly and inspection have been realized
in different manufacturing processes. These tasks have been
usually done by the human workers, but these types of
deficiencies have made a machine vision system more
attractive. Expectation from a visual system is to perform
the following operations: the image acquisition and
analysis, the recognition of certain features or objects
within that image, and the exploitation and imposition of
environmental constraints.
Scene constraint is the first consideration for the
machine vision system. The hardware for this sub-system
consists of the light source for the active imaging, and
required optical systems. Different lighting techniques such
as the structured lighting can be used for such purpose. The
process of vision system starts with the image acquiring in
which representation of the image data, image sensing and
digitization is accomplished. Image sensing is the next step
in order to obtain a proper image from the illuminated
scene. Digitization is the next process in which image
capturing and image display are accomplished. The last step
in this process is the image processing in which a more
suitable image is prepared [1]. The first aim of this article is
to show typical examples of the visions systems in the
automated manufacturing systems.
2 Operation of a machine vision system
A visual system can perform the following functions:
the image acquisition and analysis, the recognition of an
object or objects within an object groups. The light from a
source illuminates the scene and an optical image is
generated by image sensors. Image acquisition is a process
whereby a photo detector is used to generate and optical
image that can be converted into a digital image. This
process involves the image sensing, representation of image
data, and digitization. Image processing is a process to
modify and prepare the pixel values of a digital image to
produce a more suitable form for subsequent operations.
Pattern classification refers to the process in which an un-
known object within an image is identified as being part
of one particular group among a number of possible object
groups [1].
3 The components of machine vision
system
A typical machine vision system consists of several
components of the following:
• one or more digital or analogue camera (black and white
or colour) with optical lenses,
• interface the camera to digitize the image (the so-called
frame grabber),
• processor (this is usually PC or embedded processor such
as DSP),
(In some cases, all the elements listed above are included
in the one device, the so-called smart cameras).
• device I/O (input/output), or communication links (e.g.
RS-232) used to report the results of system,
• lens for taking close-ups,
• adapted to the system, specialized light source (such as
LEDs, fluorescent lamps, halogen lamps, etc.),
• software to the imaging and detection of features in
common image (image processing algorithm) (fig. 2),
• sync-sensor to detect objects (this is usually an optical or
magnetic sensor), which gives the signal for the sampling
and processing of image,
• the regulations to remove or reject products with defects.
Fig. 1 A typical vision system operation [2]

28
Fig. 2 The latest software Easy Builder (Cognex) for total
analysis of image
The image from the camera is captured by frame
grabber, which is a digitalize device (included in each intel-
ligent camera or located in a separate tab on the computer)
and convert image from a digital camera to digital format
(usually up to two-dimensional array whose elements refer
to the individual image pixels). The image in digital form is
saved to computer memory, for its subsequent processing
by the machine vision software.
Depending on the software algorithm, typically executed
several stages, making up the complete image processing.
Often at the beginning of this process, the image is noise
filtering and colours are converted from the shades of gray
on a simple combination of two colours: white and black
(binarization process). The next stages of image processing
are counting, measuring and/or identity of objects, their
size, defects, or other characteristics. In the final stage of
the process, the software generates information about the
condition of the product inspected, according to prepro-
grammed criteria. When does a negative test (the product
does not meet the established requirements), the program
gives a signal to reject the product, the system may
eventually stop the production line and send information
about this incident to the staff.
4 Application of machine vision
Machine vision systems are widely used in the manu-
facture of semiconductors, where these systems are carrying
out an inspection of silicon wafers, microchips, components
such as resistors, capacitors and lead frames [3].
In the automotive machine vision systems are used in
control systems for industrial robots, inspection of painted
surfaces, welding quality control [4, 5], rapid-prototyping
[6, 7], checking the engine block [8] or detect defects of
various components. Checking products and quality control
procedures may include the following: the presence of parts
(screws, cables, suspension), regularity of assembly, of the
proper execution and location of holes and shapes (curves
[9], circular area [10], perpendicular surfaces, etc.), correct
selection of equipment options for the implementation of
the quality of surface markings (manufacturer's numbers
and geographical detail), geometrical dimensions (with an
accuracy of a single micron) [2, 11, 12,] the quality of
printing (location and colour).
Beside listed above are other area to implement machine
vision. Figure 3 shows the simplest arrangement of the
machine vision measuring olive oil bottles on production
lines. The online defect inspection method based on machine
vision for float glass fabrication is shown in Fig. 4. This
method realizes the defect detection exactly and settles the
problem of miss-detection under scurvinesss fabricating cir-
cumstance. Several digital line-scan monochrome cameras
are laid above float glass to capture the glass image. The red
LED light source laid under the glass provides illumination
for grabbing the image. High performance computers are
used to complete the inspection task based on image process-
sing [14].
Fig. 3 The simplest arrangement of the machine vision
measuring olive oil bottles on production lines [13]
Fig. 4 Float glass inspection system [14]
Fig. 5 System configuration for validation testing of vehicle
instrument cluster [15]
Another interesting proposition is use the machine vision
system to validation of vehicle instrument cluster [15]. The
machine vision system (Fig. 5) consists of a camera, ligh-
ting, optics and image processing software. A Cognex In-
sight CCD vision sensor was selected for image acquisition
and processing, which offers a resolution of 1600 x 1200
pixels and 64 MB flash memory. The acquisition rate of the
vision sensor is 15 full frames per second. The image
acquisition is through progressive scanning. The camera can
work in a partial image acquisition mode, which provides
flexibility for selecting image resolution and acquisition
rate. The image processing software provides a wide library
of vision tools for feature identification, verification, mea-
surement and testing applications. The PatMaxTM
tech-
nology for part fixturing and advanced OCR/OCV tools for
reading texts are available within the software. The primary
source of illumination is from LED ring lights with direc-

29
tional front lighting, which provides high contrast between
the object and background. The selection of optical lens
depends on the field of view and the working distance. In
this setting, a lens with a focal length of 12 mm is used.
5 Conclusions
In the design and operation of a vision system, the image
formation and visual process, computational methods and
algorithms, depth information, image representation, and
modeling and matching must be considered. On the other
hand, the systematic consideration is important in the effi-
ciency and the performance of the selected machine. The
integration possibility, robustness, ease of operation, and
adding intelligence into the system in order to make it a
smart system are features of the advanced machine vision
systems. Application of machine vision is different and
wide, they are such:
– biometrics,
– positioning [16, 17],
– industrial production on a large scale,
– small-lot production of unique objects [18],
– safety systems in industrial environments,
– intermediate inspection (e.g. quality control),
– visual control of inventory in the warehouse and mana-
gement systems (counting, reading bar codes, storage
interfaces for digital systems),
– control of autonomous mobile robots [19],
– quality control and purity of food products,
– exploitation of bridges [20],
– retail automation [21],
– agriculture [22],
– exploitation of railways [23],
– vision systems for blind people.
Prof. Legutko Stanisław, M.Sc., DSc., PhD.,
Labudzki Remigiusz, M.Sc., PhD.,
Poznan University of Technology,
Faculty of Mechanical Engineering and Management,
Institute of Mechanical Technology,
3 Piotrowo street, 60-965 Poznan,
Poland,
Phone: +48 61 665 2577
E.mail: stanislaw.legutko@put.poznan.pl;
References
[1] Golnabi H, Asadpour A.: Design and application of
industrial machine vision systems. Robotics and Computer-
Integrated Manufacturing, 23 (2007), 630–637.
[2] Zhang Z., Chen Z., Shi J., Jia F., Dai M.: Surface roughness
vision measurement in different ambient light conditions.
15th
Int. Conf. Mechatronics and Machine Vision in Prac-
tice, Auckland (New Zeland), 2-8 December 2008.
[3] Bhuvanesh A., Ratnam M.: Automatic detection of stamping
defects in lead frames using machine vision: overcoming
translational and rotational misalignment. Int. J. Adv. Manuf.
Technology, 32 (2007), pp 1201-1210.
[4] Tsai M., Ann N-J.: An automatic golf head robotic welding
system using 3D machine vision system. Workshop on
Advanced Robotics and Its Social Impacts, Taipei, 23-25
August 2008, pp 1-6.
[5] Liao G., Xi J.: Pipeline weld detection system based on
machine vision. 9th
Int. Conf. on Hybrid Intelligent Systems,
Shenyang, 12-14 August 2009, pp 325-328.
[6] See A.: Rapid prototyping design and implementation of a
motion control integrated with an inexpensive machine
vision system. Instrumentation and Measurement Technolo-
gy Conference, vol. 3, Ottawa, 17-19 May 2005, pp 2065-
2070.
[7] Cheng Y., Jafari M.: Vision-based online process control in
manufacturing applications. Trans. on Automation science
and engineering, 5(2008)1, pp 140-153.
[8] Yradley E.: New generation machine vision: coping with
changes in light and surface quality. Computing & Control
Engineering Journal, 16(2005)6, pp 26-31.
[9] Lee M, de Silva C., Croft E., Wu J.: Machine vision system
for curved surface inspection. Machine Vision and Appli-
cations, (2000)12, pp 177-188.
[10] Tsai D.: A machine vision approach for detecting and
inspecting circular parts. Int. J. Adv. Manuf. Technology,
(1999)15, pp 217-221.
[11] Shahabi H., Ratman M.: Noncontact roughness measu-
rement of turned parts using machine vision. Int. J. Adv.
Manuf. Technology, 28 May 2009, published on-line.
[12] Jian-hai H., Shu-shang Z., Wei S.: Research on subpixel
detecting on-line system based on machine vision for inner
diameter of bearings. Int. Conf. on Robotics and Biomi-
metics, Sanya, 15-18 December 2007, pp 2049-2052.
[13] Taouil K., Chtourou Z., Kamoun L.: Machine vision based
quality monitoring in olive oil conditioning. First workshop
on Image processing theory, tools and applications, Sousse
(Tunisia), 23-26 November 2008, pp 1-4.
[14] Peng X., Chen Y., Yu W., Zhou Z., Sun G.: An online
defects inspection method for float glass fabrication based
on machine vision. Int. J. Adv. Manuf. Techn., 39(2008), pp
1180-1189.
[15] Huang Y., Mouzakitis A., McMurran R., Dhadyala G., Jones
R.: Design validation testing of vehicle instru-ment cluster
using machine vision and hardware-in-the-loop. Int. Conf.
on Vehicular Electronics and Safety, Columbus (USA), 22-
24 September 2008, pp 265-270.
[16] Zhenzhong W., Guangium Z., Xim L.: The application of
machine vision in inspecting position-control accuracy of
motor control systems. Proc. of the 5th
Int. Conf. Electrical
Machines and Systems, vol. 2, Shenyang, 18-20 August
2001, pp 787-790.
[17] Davis M., Lawler J., Coyle J., Reich A., Williams T.:
Machine vision as a method for characterizing solar tracker
performance. 33rd
Photovoltaic Specialists Conference, San
Diego, 11-16 May 2008, pp 1-6.
[18] Lee W., Jeon C., Hwang C.: Implementation of the machine
vision system of inspecting nozzle. 3rd
Int. Conf. on
Convergence and Hybrid Information Technology, Busan
(Korea), 11-13 November 2008.
[19] Wang. M., Wei J., Yuan J., Xu K.: A research for intelligent
cotton picking robot based on machine vision. Int. Conf. on
Information and Automation, Zhangjiajie (China), 20-23
June 2008, pp 800-803.
[20] Lee J. H., Lee J. M., Kim H., Moon Y.: Machine vision
system for automatic inspection of bridges. Int. Congress on
Image and Signal Processing, vol. 3, Sanya, 27-30 May
2008, pp 363–366.
[21] Bhandarkar S., Luo X., Daniels R., Tollner W.: Automated
planning and optimization of lumber production using ma-
chine vision and computed tomography. Trans. on Auto-
mation Science and engine-ering, 5(2008)4, pp 677-695.
[22] Dunn M., Billingsley J.: The use of machine vision for
assessment of fodder quality. 14th
Int. Conf. on Mecha-
tronics and Machine Vision In Practice, Xiamen (China), 4-
6 December 2007, pp 179–184.
[23] Edwards R., Barkan C., Hart j., Todorovic J., Ahuja N.:
Improving the efficiency and effectiveness of railcar safety
appliance inspection using machine vision technology. Proc.
of Rail Conference, Atlanta, 4-6 April 2006, pp 81-89.

APPLICATIONS OF MACHINE VISION

More Related Content

What's hot (19)

Viewers also liked (9)

Similar to APPLICATIONS OF MACHINE VISION (20)

Recently uploaded (20)

APPLICATIONS OF MACHINE VISION