Size Detection of Firebricks Based on Machine Vision Technology

He Junji, Shi Li, Xiao Jianli, Cheng Jun, Zhu Ying Shanghai Maritime University, Shanghai, 201306, China

junjihe@163.com

AbstractAt the present time, quality of firebricks is detected manually one by one under a tough working environment. It is urgent to invent an automatic device to do this job. In response to this situation, this paper proposed a fast firebricks size detecting method and device which was based on monocular machine vision technology. The method is based on the homographic principle between imaging plane and object plane during monocular imaging process. Processing the firebricks surface image from a high resolution camera by image processing technique such as edge extracting, linking, segmenting, fitting, etc, it is able to calculate the length and width of a firebrick precisely. The test results proved that it has obtained the precision of manual method at present time and is very robust. The structure of the detecting system is simple, efficient and practical.

Keywords- Firebricks;Size Measurement;Monocular Machine Vision;Image Processing

I. INTRODUCTION Firebricks are special materials which are used to lay a

blast furnace or a cement kiln. The special usage and high temperature environment enforce special requirements of the quality of firebricks. The error of their size, the defection of their outside surface and inside structure and etc must to some extant meet the quality requirements. Therefore it is an important step to detect the quality of firebricks during their production. Today manufactories detect their products one by one manually and the detecting tools are simple steel rulers. The outdated operation mode makes the speed of detecting slow and the precision of measurement unstable. At the same time it is a tough work for workers because of the remaining heat and high weight of firebricks. It is urgent for manufactories to adopt automatic detecting technology instead of manual one with the ever increasing demands of users.

At present, it is rare to find automatic detecting method and devices around the world. Most of the existing detecting devices are optical or photoelectric mechanical devices. One method is invented to use optical fiber sensor to measure the thickness of firebricks, which is proved to be high accurate. However each sensor can only measure the thickness of a firebrick[1]. Another method measures the thickness and the flatness of tiles with CCD sensor[2]. It can measure the thickness and flatness of a tile but requires fixed position of a tile being detected on a convey belt. There is another method which detects the size of a brick though the projection of it from a fixed light source. It is a smart device, however the position and angle to lay a brick are strictly

limited, otherwise the detecting precision would be degraded[3].

This paper proposed a method and system for fast and automatic size detection of firebricks which is based on monocular machine vision technology. The system takes a picture of a firebrick and obtains the precise size of the brick by image processing technique. It can improve the efficiency and stability of the detection greatly, which can bring the manufactories profit and improve the competitiveness of their products.

The principle and structure of the system are introduced in the first section. In the next section the image process methods of the system are presented. The experiments and results of the system calibration and detection are given in the third section. In the last section, conclusion is given.

II. STRUCTURE AND PRINCIPLE OF DETECTING SYSTEM OF FIREBRICKS

Machine vision inspection technique is detecting technology which is a simulation of human visual function. It obtains required information by means of analysis and calculation of the scene image. This technique is excellent in extracting information of two-dimensional or three-dimensional shape, contour, size, features and others of an object. The technique has the merits of high speed, high precision, abundant information, noncontact detection, etc. which has a lot of successful applications of a variety of objects and a number of industries [4,5]. The detecting system proposed in this paper is a monocular machine vision system whose structure belongs to the simplest one of machine vision systems. Since the length, width and height of firebricks to be detected is scalar parameters and they are constants in respect to standard production of firebricks, the monocular machine vision system is the most appropriate to be adopted. Not only its structure is simple but also its mathematical model, calculation method, calibration process is simple and easy to be implemented. Therefore it is easy to ensure the reliability of the system.

The basic structure of monocular machine vision system as shown in figure 1 is composed of a camera and a computer. The camera faces to one of the planar surfaces of a detected object and takes a picture, then the image is transferred to the computer where it is analyzed and calculated to get the desired information. The principle it based on is the homography of two planes in space. In other words a point in a plane of the detected object and its projection point in image plane is one to one mapping as illustrated in figure 2. Point C in the figure is the center of projection. The mapping is linear when represent in

2010 International Conference on Measuring Technology and Mechatronics Automation

978-0-7695-3962-1/10 $26.00 2010 IEEEDOI 10.1109/ICMTMA.2010.798

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homogeneous coordinates, which can be expressed as follows[6]:

=

=

111 987654

321

YX

HYX

hhhhhhhhh

vu

s

(1) Where (u,v) is the coordinate of an image point in image

reference coordinate frames O1-UV, (X, Y) is the coordinates of a point in object plane referenced in object plane coordinate frames O-XY, s is a scale factor. H is the homographic matrix of this mapping, which is in fact a matrix composed of internal and external parameters of the camera.

Figure 1. Principle of monocular machine vision system

Figure 2. Mapping between the two planes

In actual application, an additional lighting source is always added to the system in order to get a clear image. The actual structure of the system is illustrated in figure 3.

Figure 3. The system structure

III. BASIC ALGORITHM OF FIREBRICKS IMAGE PROCESSING

Most of the shapes of firebricks products are rectangle, so this paper focuses on size measurement of rectangular firebricks. As a firebrick is usually compressed out of a mold, the surfaces of it are relatively rough. The edge and corner of it are often worn out slightly within the extant of tolerance, which makes the image process more complex than expected. In addition to adoption of suitable lighting mode to get clear images, a reasonable and stable image processing algorithm must be invented. After analyzing the characteristics of the edge of firebricks in detail, a four-step method was proposed which contains edge extracting, edge linking, edge segmenting and edge fitting.

A. Edge extraction of firebricks image After reading the image and filtering out noises, we

extract edge from firebricks image by canny edge detecting algorithm. An edge image of single pixel width is obtained as shown in figure 4. Most of the actual edges are extracted from the firebrick image as shown in the figure. In addition, the edges of some texture which belongs to disturbing variable on the surface of the brick are also extracted. The edges of miniature worn part of corners are extracted too, which are usually irregular curve. It is a disturbing variable which destroys the edge fitting of the whole contour of a brick. This kind of disturbance can be reduced by adjusting the projecting angle and brightness of the light as well as increasing the color contrast between the conveyor belt and bricks.

Figure 4. Image after edge extraction

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Figure 5. Image after edge linking

B. Edge linking of firebrick image The edges gotten from the above steps are actually

discrete edge points so that edge linking must be performed before edge fitting. In the image of discrete edge points eight-neighborhood searching can be done and adjacent edge points are considered to be on the same edge line. Several sets of edge points which represent several corresponding edge lines with different length are given after the searching. There is many searching method and we used deep-first algorithm. Since the textures on a firebricks surface always have granular profile, the characteristics of their edges are large curvature and short length. Therefore most of the edges of textures on surface can be eliminated from the searching results according to the length and curvature of edge lines. Figure 5 shows the image after linking and elimination. The thick lines of different gray in the figure represent the qualified edge lines being searched.

C. Edge segmenting of firebrick image As the two adjacent edges of the brick are connected to

one edge line as shown in figure 5, it is unpractical to do line fitting directly on this set of edge line. The correct way is to split them first. The splitting point is the one with the largest curvature. So an edge segmenting step is needed after edge linking step. Each set of edge lines is checked and the curvature of each point on a line is calculated. When the curvature of a point is unusually large, the line is broken into two lines at this point. The segmented lines are illustrated in figure 6 by thick lines of different gray. As can be seen, the segmented lines are very suitable for fitting the edge lines.

D. Edge fitting It is an easy task for edge fitting on the base of good

edge segmentation. Line fitting is done to each edge line, then the parameters such as the coordinates of intersection of edges, the distance between the parallel edges, the length of edges and etc can be calculated from the equations of straight fitting lines. The unit of these parameters is pixel.

Figure 6. Edge lines after segmentation

Figure 7. Image of calibration board

IV. EXPERIMENT AND RESULTS

A. Calibration experiment and results of monocular machine vision system The calibration task of the vision system is to determine

the values of each element in the homographic matrix H. The common practice is to take the picture of planar calibration target with known size, select 4 or more feature points with image coordinates (ui, vi) and real space coordinates (Xi, Yi), substitute them into (1), establish a set of equations of unknown variable hj(j=1,,8h9=1) , solve the unknown parameters from the equation[6]. The style of our target is Checkerboard pattern format as illustrated in figure 7. We actually select 12 points to perform the calculation and the result is shown as follows:

=

100001.062.29594.3137 0.111079.80090.12064.2498

H

B. Experiment and Results of Measuring Length and

Width of Firebrick Once accurate values of elements in matrix H are

determined, the length and width measurement of firebricks can be carried out. The coordinates (X, Y) in object plane can be gotten by transforming (1) as follows:

=

=

))(())(())(())((

))(())(())(())((

82748571

374671

71857482

385682

uhhvhhvhhuhhhuvhhhvuhh

Y

uhhvhhvhhuhhhuvhhhvuhh

X

(2) According to all the edge point coordinates and corner

point coordinates of bricks gotten from the previous image processing, we can use formula (2) to calculate their corresponding points in the O-XY coordinate system, then the information such as the actual length, width, parallelism, perpendicularity and more of the firebricks can be calculated.

We carried out the calculation on the photos of the same bricks from different view angle, and pictures are shown in figure 8. The measurement results obtained are shown in table I. Two values for each length and width represent the corresponding values of the two parallel edges. The standard size of the brick should be 300mm150mm, however the

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actual length and width measured by a slide caliper change slightly with different measuring point, which is in the range (3000.5)mm(1500.5)mm.

(a)

(b)

(c)

(d)

Figure 8. Pictures of a firebrick from different view

TABLE I. LENGTH AND WIDTH OF A FIREBRICK MEASURED BY THE MACHINE VISION SYSTEM

No. Length(mm) Width(mm)

a 300.5931 300.0476 150.0905 150.3227

b 301.0740 300.0994 150.5486 150.1196

c 300.2688 298.9699 149.8153 150.5654

d 300.8835 299.7102 150.0559 150.4404

The data in table I shows that errors between the measured size and the actual size are small, which meet with quality inspection requirements. This experiment verifies the algorithm is stable.

V. CONCLUSIONS A new measuring approach of firebricks size is

proposed in this paper. A monocular machine vision system has been established and a four-step image processing method is introduced. It is characterized with simple system configuration, simple algorithm, as well as appropriate

accuracy and stable performance. It is a good start for machine vision technology to be used for firebricks quality inspection. We will continue further development on this application.

ACKNOWLEDGEMENTS The ongoing research described herein is supported by

Shanghai Maritime University Research Fund (20090169) Shanghai Emphasis Subject Project (J50602) Shanghai Maritime University Graduate FundShanghai Engineering Research Center Construction Project(09DZ2250400).

REFERENCES [1] Yang YU, Wang Rui, He Zhong, Liu Fei, Zhang YaPing, Methods

and system research of improving dynamical detective precision of firebricks thickness, Refractories, 1997,31(6):348-350(in Chinese)

[2] HU Hong-hao, LI Bing-lin, Cai Zhi-rui, Li Shi-hong, Research for the On-Line Thickness Detection of Ceramic Tile Based on CCD, Shandong Ceramics, 2006, 29(3):14-16(in Chinese)

[3] La Verne Thornton, Gerald L. Stuart, Apparatus For Sensing And Ejecting SBricks Of Improper Size, United States: Appl. No. 64,740, Aug. 18, 1970.

[4] Wei Zhenzhong, Flexible On-line 3D Coordinates Measurement System Based on Machine Vision, PHD thesis of Beijing University of Aeronautics and Astronautics, 2003. (in Chinese)

[5] Zhang Guangjun, Machine Vision, Beijing, Science Press, 2005. (in Chinese)

[6] Criminisi, Antonio. Accurate Visual Metrology from Single and Multiple Uncalibrated Images. University of Oxford, June 2001.

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