Accurate Extraction and Measurement of Fine Cracks from Concrete Block Surface Image

Atsushi Ito* Yoshimitsu Aoki** Shuji Hashimoto*

*Dept. of Applied Physics, Waseda University **Dept. of Information Science

3-4-1, Ohkubo, Shinjuku-ku, Shibaura Institute of Technology Tokyo, 169-8555 JAPAN 307, Fukasakutameihara, Saitama 330-8570, JAPAN

{ito,shuji}@shalab.phys.waseda.ac.jp yaoki@sic.shibaura-it.ac.jp Abstract – Measurement and analysis of cracks on the surface of reinforced concrete can be an effective key for diagnosing quake-proofness of a construction or measuring material fatigue. The conventional method of diagnosis is to analyze cracks using a sketch manually produced by inspection engineers, however it takes a fairly long time and lacks quantitative objectivity. This paper proposes an automatic detection and analysis system for concrete block inspection. By employing a high-resolution camera, the cracks and displacement features are automatically extracted using an integrated image processing techniques. Utilizing the proposed method, the crack scale can be measured with sub-pixel order accuracy. Automatic feature extraction and quantitative analysis of cracks are implemented to make the system efficient tool for assisting specialists in inspection procedures.

I. INTRODUCTION

Recently, the importance of image processing for visual inspection has been increasing in various fields, especially in industrial production. As for the civil and construction engineerings, visual inspection has been strongly required to examine and maintain the safety of structures [1-4]. At present, the most popular method for inspection of concrete block structures depends on the specialists’ knowledge and experience by means of sketch of cracks which is manually produced. However, the method does not only required time and effort, but also lacks objectivity for quantitative analysis. An efficient and economical method is required in order to accomplish an efficient and accurate diagnosis of concrete structures [5-7].

We have already proposed a prototype system that can extract and analyze cracks by employing image processing techniques [8]. In this paper, we propose a novel crack measurement/analysis system with improved accuracy of crack extraction. Cracks and displacement features are automatically extracted from high-resolution image by using an integrated image processing technique [9-11]. The analyzed results include the direction and width of each labeled crack, thus giving effective information for the diagnosis of reinforced concrete structures. In particular, the area of the extracted cracks is strongly required at the worksite of inspection. We also propose an interpolation method for a

sub-pixel order measurement [12].

II. OVERVIEW OF CRACK MEASUREMENT SYSTEM

The flow chart of the proposed method is shown in Fig.1. In this section, the flow of the left column of Fig.1 is explained in sequence, which is the basic method for crack extraction and analysis. The improved processes in the right column are described in the next section.

Step 1. Image Acquisition:

A linear scan high resolution CCD camera is used to photograph the surface of a concrete block. Fig.2 (a) shows a typical original image for input. The size of the concrete block is approximately 100cm x 40cm, while the CCD camera has 3040 x 2008 pixels with 8 bit gray level. Therefore a pixel represent an area of approximately 0.33 x 0.33 mm which maybe sufficient for most applications. However in the diagnoses of concrete block structure, because the minimum crack width to be inspected is less than 0.1 mm, the system must detect the sub-pixel size cracks for practical use.

Image Acquisition

Shading Correction

Preliminary Thresholding

Further Thresholding

Thinning

Crack Tracking and Labeling

Features Analysis

Crack Area Expansion

Summation of Brightness

Re-selection

Fig.1 Flow of the measurement

Step 2. Shading Correction In this step, the non-uniformity of lighting that occurs in the photographing procedure is removed. As the correction data for the shading, we capture a white board image in advance (See Fig.2 (b)). By dividing the original image data with this correction data in each pixel, the shading correction in performed as shown in Fig.2 (c).

Step 3. Preliminary Thresholding The preliminary thresholding is performed with a fixed threshold to obtain a coarse binary image of the surface (Fig.2 (d) and Fig.3 (a)). The threshold k is set as follows based on the statistical analysis of the image.

k = ( Ave + Min ) / 1.25 Ave : average of brightness in the whole area Min : minimum value of brightness in the whole area

Step 4. Further Thresholding

We assume that if certain number of black pixels exists in the 9 x 9 pixels around a black pixel in the preliminary thresholding image, then it is probable that an undetected crack exists in that region. Further thresholding is performed in such region employing the discrimination analysis method which determines the local threshold so as to maximize the inter class distance in the region [9]. Fig.3 (b) shows the further thresholding result of Fig.3 (a).

Step 5. Thinning [10] To extract the geometrical structure or direction of a crack, a thinning procedure is employed to reduce the crack width into one pixel size as shown in Fig.3 (c).

Step 6. Crack Tracking and Labeling From the starting point, such as an edge or fork point of a crack, the crack is traced until the terminating point (another edge or fork point) is detected and then labeled [11]. The example of the crack division labeling is shown in Fig.4.

Step 7. Features Analysis From data obtained in the previous steps, the direction and area of cracks are calculated for each labeled crack. Then the statistics of the cracks are also calculated in this step for crack length, thickness and direction.

III. SUB-PIXEL ORDER INTERPOLATION

Using the method mentioned in Section II, we can extract cracks from the picture and analyze their features. However,

Fig.3 Example of Further Thresholdingand Thinning procedure

Further

Thresholding

Thinning

(a) (b)

(c)

Thresholding

Thinning

(a) (b)

(c)

1 1 1 1 1 1 1 1

1 1 1 1 1111

AAA B AA BB

AABBBCCC

1 1 1 1 1 1 1 1

1 1 1 1 1111

AAA B AA BB

AABBBCCC

Fig.4 Example of Labeling procedure

(a) Input Image (b) White Board Image

(c) Shading Image (d) Binary Image

Shading

Thresholding

(a) Input Image (b) White Board Image

(c) Shading Image (d) Binary Image(d) Binary Image

Shading

Thresholding

Fig.2 Example of Shading Correction and Preliminary Thresholding procedure

analyzed features of a crack (length and area) can be measured in pixel-scale.

We examined the accuracy of the area measured by the above method using a standard. Firstly, a transparent scale called “crack scale” is attached on the concrete block surface in the image acquisition step (Fig.5 (a)). There are several black scales with their accurately measured area (mm2) printed on the sheet. In the above method, we regarded the extracted black pixels of the binary image as the region of the crack therefore the number of pixels in the extracted black region directly indicates the area of the crack. The black regions in the transparent scale are treated as ordinary cracks, whose area can be extracted using the proposed method, so that the relation between the area of a crack in pixel-scale and its physical size can be obtained.

Utilizing this idea, we counted the number of extracted black pixels for several scales on the crack scale after the thresholding processing. As a result, there are cases that the thicker scales have less number of extracted pixels than thin scales. Especially, this tendency can often be seen for the thin scales between 1 to 4 pixels width. We consider this error is caused by the quantization error in the thresholding step as follows.

Fig.6 (a) is the enlarged crack scale of a gray scale image after the shading correction, and Fig.6 (b) is the binary image after the execution of threshold processing. Comparing these images, it is found that the brightness values of the pixels in the region determined as the crack region are not equal. The reason is that if the target pixel is completely included in the actual crack region, it is judged as a black pixel. In the case that the target crack has the scale of sub-pixel order, the

brightness of the pixel appears as a gray level value. The gray level value pixel may include the crack area. The classification of the pixel depends on the threshold value so that the pixel may be classified as the non-crack region even if it covers the crack region.

To enable a fine measurement for such sub-pixel order cracks, we propose a sub-pixel interpolation method using the total brightness of a gray scale image.

Firstly, we expand the black region by one pixel in the binary image to include the omitted crack region, as a gray region as shown in Fig.6 (c).

Secondly, the region corresponds both of black and gray pixel regions in the image Fig.6 (c) is selected in the gray scale image as shown in Fig.6 (d). The brightness values in the selected region in Fig.6 (d) are summed up as the total brightness of the crack region which can be used as an area parameter of the crack.

Finally, the total brightness value can be converted to the physical crack area using calibration data obtained from standard measurement.

IV. COMPUTATION OF CALIBRATION LINE

We examined two methods for the crack scale sheet in order to investigate efficiency of the proposed method. One is the binary method of counting the number of black pixels after thresholding in the crack (Method 1), and the other is the grey-level method using the total brightness values in the crack region (Method 2).

Fig.7 shows the analysis results of both methods. The horizontal axis is the physical area (mm2) of the scales on the crack scale sheet, and the vertical axis of Fig.7 (a) and (b)

Reselection

(d)

Thresholding

Expanding

(a) (b)

(c)

Fig.6 Selection of crack region

(a) Input image

(b) Crack scale

(c) Gray scale image (d) Binary image

Thresholding

Fig.5 Setting of crack scale

indicate the number of black pixels and the total brightness values in the crack region, respectively. In these figures, the solid lines are obtained by the least-squares method. These regression lines are used as the calibration lines for actual crack measurement.

As a result, the values of multiple correlation coefficients are 0.89613 for Method 1 and 0.98533 for Method 2, so that the utilization of brightness values (Method 2) can represent the crack area more accurately.

V. IMPROVEMENT OF AREA MEASUREMENT The methods described above are applied to actual crack

measurement. Fig.8 (a) shows the target image of cracks on the concrete surface. As shown in Fig.9, pixels in the crack have nonuniform brightness values caused by the depth fluctuation of cracks. Considering such case, Method 2 is not efficient because it assumes that the crack area has uniform brightness. Contrarily, Method 1 is effective provided that the crack area is classified correctly. Therefore, we introduce Method 3 that mixes the advantages of Method 1 and Method 2. In this method, for the pixels in the inner region of cracks, we assign constant gray brightness value as in Method 1, and

for the pixels in the boundary region, we use the brightness as in Method 2. And the brightness values of all pixels in the crack region are summed up, and the physical area of the

Fig7. Calibration lines

0

20

40

60

80

100

120

0 2 4 6 8 10

Area (mm2)

Num

ber o

f bla

ck p

ixel

s

(a) Method 1

(b) Method 2

02000400060008000

100001200014000

0 2 4 6 8 10

Area (mm2)

Sum

mat

ion

of b

right

ness

Fig.8 Crack image for measurement

(b) Cracks with different thickness

A C

B

D

A D

B

C

(a) Test image and measurement areas

Thresholding

Fig.9 Example of Nonuniform brightness

region can be computed using the calibration line for Method 3.

The measurement areas from A to D in Fig.8 have different thickness cracks. Three methods were applied to cracks in these areas. Using Method 3, the brightness value of the black pixels in Fig.6 (c) are equally set to 250 (the average brightness value of uniform black pixels), and those of surrounding pixels are used from gray scale image as shown in Fig.6 (d).

The measurement results are summarized in Table.1. As the correct value of the area of the crack region cannot be obtained precisely, the results are also illustrated with the acquired images in Fig.10. In these figures, the measurement results are displayed with linear parallel lines of which distances are calculated from the measured areas and the lengths of the crack segments. In regard to a fine crack (Fig.10 (d)), the result from Method 2 and 3 takes large value that are close to correct extracted value by means of visual inspection of specialists, although the quantitative area of the cracks were not available. The measurement of total value of the brightness containing fine structure in sub-pixel order is

effective in comparison with Method 1. On the other hand, for a thick crack (Fig.10 (b)) that occupies a large region, the result from Method 1 and 3 takes large value that is close to correct value.

From these experiments, we can conclude that Method 3 is the most reliable method and can be applied to wide range of crack image with minimum variance regarding the condition of given image.

Although some additional processes to refer the gray scale image are required in Method 2 and 3, the differences in processing time are not significant.

Method.1 Method.2 Method.3A 5.097 8.911 8.791

B 27.516 22.789 26.88

C 7.832 10.492 10.361

D 2.425 5.554 3.423

Table.1 Measurement result of crack areas (mm2)

Fig.10 Measurement results

Method 1

Method 2

Method 3

B

A C

D

VI. CONCLUSION

We proposed an automatic measurement system for the concrete brocks inspection by means of fine crack extraction. By employing a high-resolution camera, features of cracks are automatically extracted using an integrated image processing techniques. Employing threshold selection and total brightness of the crack region, we can reduce the limitations of the extraction to sub-pixel order accuracy. This means the proposed method enables not only the extraction of very subtle cracks, but also realizes high quality crack analysis. The proposed system provides a quantitative analysis with satisfactory quality to construction engineering specialists in inspecting cracks.

At present, the implemented system is used for the measurement and analysis of the cracks in the loading test of concrete blocks, and has obtained promising evaluations from the professional inspectors.

The implemented system works under the Win2000 operating systems (PC Pentium III 600MHz). The total computation time of all procedures is less than one minute.

We plan to implement the proposed method for prediction of material degradation, supporting the diagnosing method for earthquake-resistance constructions, earthquake-hit buildings and so on. One of the main future works for practice applications is to investigate the extraction method from the image that contains the dirt spots and textures.

VII. ACKNOWLEDGMENTS

The authors wish to thank Dr. Ouchi, Dr. Yamada and Mr. Takeda of Obayashi Technical Research Institute for their valuable suggestions and encouragement to this study.

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