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Texture digital analysis for corrosion monitoring
Accepted Manuscript Texture digital analysis for corrosion monitoring Flávio Felix Feliciano, Fabiana Rodrigues Leta, Fernando Benedicto Mainier PII: DOI: Reference:
S0010-938X(15)00025-6 http://dx.doi.org/10.1016/j.corsci.2015.01.017 CS 6167
To appear in:
Corrosion Science
Received Date: Accepted Date:
9 November 2014 6 January 2015
Please cite this article as: F.F. Feliciano, F.R. Leta, F.B. Mainier, Texture digital analysis for corrosion monitoring, Corrosion Science (2015), doi: http://dx.doi.org/10.1016/j.corsci.2015.01.017
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Texture digital analysis for corrosion monitoring Flávio Felix Feliciano
Mechanical Engineering Post-Graduation Program, Universidade Federal Fluminense, Niterói, Brazil
R. Passo da Pátria, 156, Niterói, Rio de Janeiro, Brazil, [email protected] +55.22.988.364.396 Instituto Federal Fluminense, Cabo Frio, Brazil
Estr. Cabo Frio-Búzios s/n, Cabo Frio, Rio de Janeiro, Brazil, [email protected] +55.22.2645.9500
Fabiana Rodrigues Leta
Mechanical Engineering Department, Universidade Federal Fluminense, Niterói, Brazil
R. Passo da Pátria, 156, Niterói, Rio de Janeiro, Brazil, [email protected] +55.21.2629.5460
Fernando Benedicto Mainier
Chemical Engineering Department, Universidade Federal Fluminense, Niterói, Brazil
R. Passo da Pátria, 156, Niterói, Rio de Janeiro, Brazil, [email protected]
ABSTRACT This article proposes the use of texture analysis, an automated optical inspection technique, for nondestructive surface corrosion monitoring. It presents an ASTM A36 steel atmospheric corrosion test conducted over 44 days to obtain a photo sequence of corrosion evolution. These images have been processed for six textural characteristics, and the values are presented as "feature variation versus time" graphs. The results show that the technique is feasible as a new method to check the surface corrosion state.
Keywords
A: carbon steel; C: atmospheric corrosion; C: rust Other keywords (not in instructions list):
texture; automated optical inspection; corrosion monitoring
1. Introduction It is not hard to find records of losses and accidents caused by corrosion. This demonstrates the importance of corrosion control. The key to control is achieving information about corrosion evolution through monitoring corrosion. In monitoring, consecutive data on the material degradation rate are acquired and decisions based on this information become possible. There are several techniques to detect, measure and predict corrosion, most of which have evolved in recent decades. Among the non-destructive testing methods for corrosion monitoring are eddy-current, ultrasonic inspection, acoustic emission, vibration analysis, radiography, thermography and visual inspection [1]. Due to the ease of monitoring, visual inspection is still employed with satisfactory results because it informs about the type and extent of the corrosion. This approach becomes more satisfactory when the focus of analysis is the metal surface characteristics and textural modification, as in the conversion of rust with atmospheric corrosion of the steel surface (brown oxide) [2]. However, visual inspection has subjective criteria because it relies on the human eye. Today in industries, following technological evolution, the amount of automated systems is increasing and their accuracy is improving, especially with regard to repetitive and tedious processes. Under repetitive conditions, humans are especially susceptible to mistakes. In this sense, one of the growing tendencies is the use of machine vision, which seeks to have machines extract visual information, in other words, aims to make machines see, replacing the human visual system. With automated optical inspection systems, it is possible to maintain high performance and a low error rate and to analyse details that humans cannot perceive.
This work proposes the application of automated optical inspection techniques to extract quantitative information about surface corrosion status. Specifically, machines that conduct texture characteristic analyses for images are employed. Our aim is to demonstrate that for a given area, monitoring the changes in images over time makes it possible to show how corrosion evolves over time. The main contribution of this proposal is the possibility of using texture digital image analysis as a complementary tool for conventional corrosion monitoring techniques, for example, in the detection of the moment in which surface recovering must be done or metallic components must be replaced. Corrosion and automated optical inspection are old and well-developed research areas. The interface between these subjects is not recent either. Application of automated optical inspection to analyse corrosion was already proposed in the 1980s by Itzhak, and Dinstein Zilberberg [3]. However, the integration of these research areas still rarely appears in the literature, with only a slight increase in recent years. An overview of the literature on automated optical inspection for corrosion analysis is presented in table 1. The authors were classified according to their approaches. Although several authors propose specific applications, we grouped them into three categories. The first one includes approaches relating to analysing the corrosion evolution. The second addresses the recognition of the type of corrosion or if there is a principle of oxidation. The third category includes corrosion measurement based on some parameter or standard. Table 1. – Automatic optical inspection (AOI) approaches applied to corrosion analysis. AOI technique Boundary or shape analysis Image Correlation Texture Fractal geometry Another approaches Transforms
Application in corrosion Evolution analysis Recognition Measurement Evolution analysis Recognition Evolution analysis Recognition Evolution analysis Measurement Evolution analysis Recognition Measurement Recognition Measurement
Reference [3, 4] [5, 6, 7] [8, 9, 10, 11, 12] [13] [14] This approach [15] [16] [17, 18] [19] [20, 21, 22, 23] [24, 25, 26, 27, 28, 29] [30, 31, 32, 33] [34]
This work presents an atmospheric corrosion test conducted over 44 days to obtain a photo sequence of 24 samples obtained from 3 ASTM A36 steel specimens. Once these images were processed, time values for six textural characteristics were obtained. These values are presented in the graph "feature variation versus time". All test parameters and techniques are described below.
2. Texture analysis methods Automated optical inspection is a machine vision application used in inspection and quality control. It allows machines to extract visual information, replacing the human eye. With these systems, machines can analyse details that the eye and the human mind could not realize in quick analysis. The advantages of these vision systems are their accuracy and comprehensiveness, and they maintain high performance in various industrial and commercial activities. Texture analysis is one of the automated optical inspection approaches that extracts surface feature information. This technique is compatible with the evaluation of corrosion because the oxidation process starts at the surface of the material and changes its characteristics gradually over time.
“The texture is among the features used by the human visual system, containing information about spatial distribution and luminosity variation and describing the structural surface arrangement in relation to neighbouring regions” [35]. The texture is characterized by the image repetition in a given region. The textel (texture element) is defined as the basic building block of texture, i.e., the smallest digital image area that composes a distinct texture. The textel can be repeated on the image with variations in size, intensity, colour and orientation and still contain noise. The purpose of the texture analysis is to identify the neighbourhood of these similar elements that characterize the connectivity, density, and homogeneity. The human eye easily recognizes texture, however, to develop digital processes able to measure and describe texture is extremely complex. Various techniques can be found in the literature to identify texture features [35, 36]. Most of them are based on greyscale pixels. The most appropriate for each application should be defined based on the context because despite the existence of several methods, none are able to effectively target all types of texture. The application proposed here does not employ any technique to identify the texture directly. It uses texture measures that are expressed by numerical values and are able to quantify certain characteristics, such as entropy, Hurst coefficients, correlation, energy, or homogeneity, to observe how these characteristics evolve with corrosion. These characteristics and their methods of calculation are detailed below. 2.1. Entropy
Entropy is a statistical measure of randomness that can be used to characterize the texture of the input image and is defined as: entropy = ∑ · log
(1)
where contains each image histogram value. In a 256 greyscale image, i goes from 1 to 256. 2.2. Hurst coefficient
The Hurst coefficient is used as an approximation of the fractal dimension for images in grey levels. The fractal dimension can be used for determination of surface roughness, wear, erosion, corrosion and more. It is useful to characterize texture with numerical indices. The Hurst coefficient is calculated for a part of the image using the following steps: First consider the Euclidean distance d of each pixel i to the central pixel c. , ; ,
(2)
Then, the pixels are organized into n groups by Euclidean distances d. For each group, calculate the difference dif between the greatest grey level and the lowest grey level found. Calculate logarithmic coordinates ln , ln for each group. Finally, using the least squares method, set the straight line defined by the logarithmic coordinates. The coefficient a is the desired Hurst coefficient:
∑ ∑ ∆ ∑ ∆
∑ మ ∑ మ
(3)
2.3. Spatial co-occurrence texture features
In addition to the distribution, these measures take into account the pixels’ spatial relationship. The co-occurrence matrices are defined in such a way that each element of this matrix represents the frequency , at which a pixel with a grey level i and another with grey level j occur in an image
separated by a distance d in the theta direction. From these co-occurrence matrices, it is possible to calculate numerical values called descriptors that provide information about the original image; among them are probability, entropy, differences moments, energy, variance, correlation, homogeneity and others. In this research, only the following were used: Contrast. This is the measure of intensity contrast between a pixel and its neighbour. contrast ∑,| | ,
(4)
Correlation. This is the statistical measure of how correlated a pixel is to its neighbour over the whole image. Note that is the standard deviation and is the mean value of the elements. correlation ∑,
,
(5)
ೕ
Energy. This is the sum of squared elements in the co-occurrence matrix. energy ∑, ,
(6)
Homogeneity. This is the measure of closeness of the distribution of elements in the co-occurrence matrix. homogeneity ∑,
, | |
(7)
3. Materials and methods Several associations, societies and companies such as ASTM, NACE, ABNT and Petrobras (Brazilian Oil Company) define standards for testing, inspection and monitoring procedures. Those considered relevant to the tests and analyses used and proposed here are ASTM [37, 38, 39, 40] and NBR [41, 42, 43, 44]. Among these, ASTM G50 (40) and NBR 6209 (42) are specifically geared towards observing the performance of the test: the mass gain/loss and localized attack depth. As this work focuses on modifying the surface texture and the tests are non-destructive, there was no need to follow the parameters related to mass and attack depth measurements or the removal of the specimens. Exposure conditions such as inclination, surface orientation, absence of vegetation near the atmospheric panels and no contact with conductive material were kept exactly as prescribed in atmospheric test standards [40, 42]. For the same reason, other parameters, such as the specimen size, its surface preparation and degreasing, atmospheric environments, the number of test specimens required and the total exposure time had to be adapted as set out below. The specimens used were ASTM A36 carbon steel in free contact with the atmosphere, i.e., without any type of cathodic protection, passive layer or painting. These were obtained from 100x100 mm square pieces cut from 2-mm-thick plates as shown in figure 1. To ensure the original steel properties, discarding any pre-existing corrosion or encrustations that could be included on the surface, the specimens were prepared by removing a thin layer using a solid remover disc composed of synthetic fibre, resin, and silicon carbide. As the surface preparation was done immediately before the test started, degreasing was not necessary. After removing the surface layer, only clean dry airflow was used in preparation to start the test as in NBR 6210 [43]. The abrasive method associated with slow rotation is not able to create the conditions to change metallurgical characteristics; however, heat is generated in the cutting process. To disregard this effect, an edge of 15 mm around the specimen contour was left out of the results analysis, leaving only a useful 70x70 mm square region as shown in figure 1. For identification, small drilled holes were made in the edge. For orientation in later acquired images, one chamfer was left in the vertice.
Fig. 1. Specimen dimensions. 1- Chamfer to position mark. 2 - Identification holes. 3 – Useful area. 4 – Discarded edge.
The test site is an urban area (22º52'46" S and 42º01'07" W), approximately 1000 metres away from the sea waves formation line, in the city of Cabo Frio in Rio de Janeiro, figure 2. This place has both urban and marine atmospheres, little rain, 500 mm/year average pluviometry and high intensive NE and S-SW winds, both blowing from sea to land with 5.6 m/s average speed and reaching up to 10 m/s [45, 46]. The deposition of chlorides produced by marine aerosol is increased and has greater reach due to these high winds. This combination of factors contributes to corrosion.
Fig. 2. Test site, urban area of Cabo Frio/RJ, Brazil. Google [47]
In a harsh, especially adverse environment, lacking any corrosion protection, the ASTM A36 steel presents rapid modification in its features on the very first days of exposure. Exposed to rain and dew, the changes in the rust layer become abrupt. That’s why special attention has been paid: a covering was used to protect the specimens; thus, specimens were exposed to chlorides, atmospheric temperature and wind action but were free of any precipitation. A higher frequency was used for capturing images in the beginning, which was reduced over time, as in figure 3. For the first two days, three photos were taken per day; for the next three days, two per day; and from the sixth to the twelfth, one per day, From then on, photos were taken every three days and then every four days according to the following scheme. The result was twenty-four photos of each specimen over a 44day time interval.
Fig. 3. Timeline of photos taken over the 44 testing days. Higher frequency has been used in the beginning because there is rapid modification of the features on the first days of exposure.
For image acquisition, to avoid external lighting influences, one chamber light was used. Figure 4 shows this chamber design that includes LEDs for lighting control, camera and specimen positioning and other conditions to standardize images:
Fig. 4. Chamber to capture images under light control and position. AA) Lateral section; BB) Lid view; CC) Leds section; DD) Internal basis section; 1) Chamber Lid; 2) Camera side guide; 3) Camera; 4) Lid hole to fit the camera lens; 5) Antireflective shield; 6) Crystal LEDs; 7) Specimen side guide; 8) Specimen; 9) Chamber Base; 10) Fiducial marks; 11) Colour calibration.
Image registration is a geometrical transformation that relates the row and column coordinates between two images to eliminate existing misalignments. As the capture system is not able to ensure precise alignment, a step of image registration is required. For this reason, fiducial marks were included in the chamber. The photos were acquired by a digital colour camera with 14 megapixels. Due to shooting conditions, camera placement and 70x70 mm useful area for specimen analysis, the cropped photo (equivalent to the useful area) covers 3.06 Mpixels in a 1750x1750 pixel square region (653 dpi). For digital analysis, a 500x500 pixel greyscale sample was used (20x20 mm). Due the difference between specimen useful area and sample image, it is possible to extract eight samples from each specimen, for a total of 24 samples.
4. Results and Discussion Twenty-eight images were taken throughout the test from each of the 24 samples. Figure 5 shows only 10 images of the same sample. After processing, these images generated a numerical results value table for each textural characteristic considered. For example, table 2 shows part of the entropy values. Observe that the columns present the values of the texture features for each sample (A to H) obtained from 3 specimens (1 to 3). The lines show the 28 different instants in which the photos were taken. For each instant, the arithmetic mean and the standard deviation of the 24 samples were calculated and those results plotted in graphs (figure 6 to 11). Here the arithmetic mean for each instant is being represented by dots, while standard deviations by scatter bands.
Day 1
Day 2
Day 3
Day 5
Day 6
Day 8
Day 11
Day 15
Day 30
Day 44
Fig. 5. The atmospheric corrosion evolution in an ASTM A36 sample. Here, only 10 of the 28 images generated over the 44 days of testing are presented.
Table 2 – Summary of values for entropy. Columns present values for each sample (A to H) obtained from each specimen (1 to 3), lines present the test instants. Mean and standard deviation were used to generate the graphs.
28 taken images
Day 1, 7h Day 1, 15h Day 1, 23h Day 2, 7h Day 2, 15h ڭ Day 4, 19h Day 5, 7h Day 5, 19h Day 6, 7h Day 7, 7h ڭ Day 34, 7h Day 38, 7h Day 42, 7h Day 44, 7h
1-A 4,07 4,22 4,64 4,86 4,90 ڭ 6,60 6,73 6,89 7,00 7,10 ڭ 6,01 5,97 5,84 5,72
1-B 4,53 4,58 4,91 5,00 5,03 ڭ 6,54 6,73 6,83 6,94 6,99 ڭ 6,06 6,02 5,90 5,78
1-C 4,11 4,30 4,87 4,95 5,09 ڭ 6,76 6,94 7,05 7,15 7,16 ڭ 5,98 5,94 5,83 5,72
ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ
2-B 4,05 4,20 4,52 5,51 5,95 ڭ 7,08 7,17 7,17 7,22 7,16 ڭ 6,09 6,00 5,79 5,70
24 samples 2-C 4,03 4,25 4,61 5,59 6,06 ڭ 7,13 7,19 7,19 7,18 7,08 ڭ 6,09 6,01 5,80 5,70
2-D 4,33 4,46 4,65 5,44 5,82 ڭ 6,96 7,04 7,03 7,06 7,10 ڭ 6,00 5,97 5,78 5,71
ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ
3-F 3,83 3,85 4,38 5,46 5,75 ڭ 6,66 6,94 7,01 7,09 7,12 ڭ 5,94 5,65 5,56 5,50
3-G 4,77 4,74 5,01 5,75 5,97 ڭ 6,66 6,87 6,95 7,03 7,05 ڭ 6,01 5,78 5,71 5,62
3-H 4,94 4,96 5,21 5,96 6,19 ڭ 6,86 7,06 7,13 7,20 7,19 ڭ 6,15 5,90 5,81 5,76
Values for graph Mean StD 4,35 0,40 4,44 0,37 4,81 0,28 5,42 0,36 5,67 0,46 ڭ ڭ 6,84 0,25 6,99 0,19 7,06 0,16 7,12 0,14 7,14 0,13 ڭ ڭ 6,05 0,10 5,94 0,15 5,80 0,14 5,71 0,13
The texture features studied were Entropy, Hurst coefficient, Contrast, Correlation, Energy and Homogeneity. The last four were calculated from co-occurrence matrices. Thus, each was calculated for three different co-occurrence lengths: 2, 5 and 10 pixels. The values for each characteristic in a given co-occurrence length were based on the mean value for the co-occurrence in the vertical and horizontal direction. Entropy and Hurst coefficients were calculated directly from the sample image. Its behaviour over time is presented in figures 6 and 7.
Fig. 6. Entropy as a function of time (dots and scatters bands show mean and standard deviation for different sample values).
It is possible to observe that the entropy values form a set of points that present an over-time behaviour that can be understood as a curve. Additionally the values have a low standard deviation. This behaviour can in some ways be associated with visual changes in steel. Once the value of image entropy represents the randomness of the greyscale, the graph shows the increased randomness in accordance with the increase in the contrast between the amount of oxide on the surface (dark spots) and the metal (light areas). Around the 10th day, the oxide begins to cover the entire surface, thus, decreasing entropy. At the end of the test, it is clear that, although the oxide has taken the entire surface, it does not have a uniform colour, which contributes to the graph tendency to stabilize at an intermediate value.
Fig. 7. Hurst Coefficient as a function of time (dots and scatters bands show mean and standard deviation for different sample values).
The Hurst coefficient is based on a process that tries to obtain a numerical value to identify a shape or texture element in the picture, although a good feature to identify texture does not present a suitable behaviour for the approach presented here, as it presents a very varying mean and the standard deviation between samples is too high. This behaviour is driven by the corrosion evolution.
Such evolution does not follow a standard, like stripes or spheres. Instead, a pattern of random spots can be seen. For the four texture features studied, which were based on the co-occurrence matrices, the results also showed an interesting behaviour in figures 8, 9, 10 and 11. Like the entropy, they present a set of points that can be observed as a curve. When comparing the results for the three co-occurrence values studied in each feature, it is noted that the differences in distances of co-occurrence only result in differences in the features’ values; there is little variation in the behaviour of the curve points. This conclusion becomes more evident when observing the comparative graphs below.
Fig. 8. Contrast as a function of time. Specifically, 2-, 5- and 10-pixel co-occurrence length and comparative (dots and scatters bands show mean and standard deviation for different sample values).
The Contrast expresses the difference in intensity between each pixel and its neighbours. As it is in entropy, the bigger the quantity of dark spots, the higher the contrast value. When the dark spots reach the whole surface, the measured contrast no longer represents the metal-oxide difference, but the oxide-oxide difference.
Fig. 9. Correlation as a function of time. 2-, 5- and 10-pixel co-occurrence length and comparative (dots and scatters bands show mean and standard deviation for different sample values).
The correlation shows to what extent a pixel is correlated to their neighbours, that is, the greyscale similarity is what counts: the closer to the shade of grey, the higher the correlation value. Therefore, graphs now show an inverse behaviour. At first, the surface has uniform colour and high correlation. However, this value tends to decrease, depending on what the oxide spots look like. In other words, oxide predominance is what interferes in such correlation. Both in contrast and in correlation, graphs differ widely, according to the increase in the cooccurrence distance. This happens because the oxide spot occupy a group of pixels. When comparing a pixel inside the oxide spot to a neighbour being two pixels far, that neighbour is very likely to be in the same oxide spot. For a greater distance, the comparison is drawn by means of a pixel that is outside the oxide spot.
Fig. 10. Energy as a function of time. 2-, 5- and 10-pixel co-occurrence length and comparative (dots and scatters bands show mean and standard deviation for different sample values).
The matrix of co-occurrence presents major values when there is high frequency of the same combination of grey tones. When the oxide is dispersed in points, this frequency falls. The energy is the sum of the squares of the values in the co-occurrence matrix. So, the image being uniform, it will be high. Once we disconsider the neighbourhood, the distance of co-occurrence has little influence.
Fig. 11. Homogeneity as a function of time. 2-, 5- and 10-pixel co-occurrence length and comparative (dots and scatters bands show mean and standard deviation for different sample values).
Homogeneity and energy are analysed in much the same manner. However, the similarity in the repeated grey tones influences the result. This makes homogeneity and energy graphs resembling. However, the co-occurrence distance does influence more. All graphs show the curves have a maximum or a minimum around the 5th or 10th test day. In Figure 5, around this day, approximately half of the surface is covered by oxide. This feature results from the way the oxide scatters during this period. The method can demonstrate the metal surface conversion to oxide. The values taken as change indicators reach their extreme precisely in the middle of this transformation. These maximum and minimum episodes at the beginning of the test are somehow predictable, other corrosion monitoring methods, such as the corrosion rate obtained by the gain or loss in mass, also show a similar, more marked behaviour at the beginning. 4.1. Conclusions
The results show that texture analysis is feasible as a new method for checking the state of surface corrosion. According to this analysis, of the six characteristics studied, only the Hurst coefficient presents data varying in a way that does not behave as a curve that can be compared to the corrosion process evolution.
We aim to use these texture features to define the curves that express the corrosion evolution and therefore the surface degradation. When the curve for a material that is subject to a given environment is known, it will be possible to give an indication of the surface deterioration rate, from a simple photo, through comparison. Being visual, the method might be applicable for both occasional visual inspections and complex procedures for monitoring corrosion. Because it is done by computer, the technique allows more control, precision and it is not susceptible to human error. It is a non-destructive method, and it only requires a camera and a processor that is capable of performing relatively simple algorithms, what represents an inexpensive and not sophisticated alternative as is the case of the specimens in atmospheric panels. Once the control of the light and the correct positioning of the camera are ensured, the analysis of the structures outside the laboratory becomes possible, which eliminates the specimens needed. The technique is limited to examining the material surface conditions. It cannot, for example, identify the oxide layer thickness or the pitting depth. In some cases, this may mean a relatively generalized and relatively small corrosion. In case of abrupt changes in the corrosion rate, the method is not seen as efficient enough.
5. Acknowledgements This research was supported by the Universidade Federal Fluminense (UFF), the Instituto Federal de Educação Ciência e Tecnologia Fluminese (IFF) and the funding source was provided by Brazilian agency CAPES.
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Highlights
• • • • •
Use of texture analysis is feasible for non-destructive surface corrosion monitoring Changes in image characteristics show how corrosion evolves over time We use entropy, Hurst coefficient, contrast, correlation, energy and homogeneity. Curves present behaviour that can be compared to the corrosion process evolution. The technique is feasible as a new method to check the surface corrosion state.
S0010-938X(15)00025-6 http://dx.doi.org/10.1016/j.corsci.2015.01.017 CS 6167
To appear in:
Corrosion Science
Received Date: Accepted Date:
9 November 2014 6 January 2015
Please cite this article as: F.F. Feliciano, F.R. Leta, F.B. Mainier, Texture digital analysis for corrosion monitoring, Corrosion Science (2015), doi: http://dx.doi.org/10.1016/j.corsci.2015.01.017
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Texture digital analysis for corrosion monitoring Flávio Felix Feliciano
Mechanical Engineering Post-Graduation Program, Universidade Federal Fluminense, Niterói, Brazil
R. Passo da Pátria, 156, Niterói, Rio de Janeiro, Brazil, [email protected] +55.22.988.364.396 Instituto Federal Fluminense, Cabo Frio, Brazil
Estr. Cabo Frio-Búzios s/n, Cabo Frio, Rio de Janeiro, Brazil, [email protected] +55.22.2645.9500
Fabiana Rodrigues Leta
Mechanical Engineering Department, Universidade Federal Fluminense, Niterói, Brazil
R. Passo da Pátria, 156, Niterói, Rio de Janeiro, Brazil, [email protected] +55.21.2629.5460
Fernando Benedicto Mainier
Chemical Engineering Department, Universidade Federal Fluminense, Niterói, Brazil
R. Passo da Pátria, 156, Niterói, Rio de Janeiro, Brazil, [email protected]
ABSTRACT This article proposes the use of texture analysis, an automated optical inspection technique, for nondestructive surface corrosion monitoring. It presents an ASTM A36 steel atmospheric corrosion test conducted over 44 days to obtain a photo sequence of corrosion evolution. These images have been processed for six textural characteristics, and the values are presented as "feature variation versus time" graphs. The results show that the technique is feasible as a new method to check the surface corrosion state.
Keywords
A: carbon steel; C: atmospheric corrosion; C: rust Other keywords (not in instructions list):
texture; automated optical inspection; corrosion monitoring
1. Introduction It is not hard to find records of losses and accidents caused by corrosion. This demonstrates the importance of corrosion control. The key to control is achieving information about corrosion evolution through monitoring corrosion. In monitoring, consecutive data on the material degradation rate are acquired and decisions based on this information become possible. There are several techniques to detect, measure and predict corrosion, most of which have evolved in recent decades. Among the non-destructive testing methods for corrosion monitoring are eddy-current, ultrasonic inspection, acoustic emission, vibration analysis, radiography, thermography and visual inspection [1]. Due to the ease of monitoring, visual inspection is still employed with satisfactory results because it informs about the type and extent of the corrosion. This approach becomes more satisfactory when the focus of analysis is the metal surface characteristics and textural modification, as in the conversion of rust with atmospheric corrosion of the steel surface (brown oxide) [2]. However, visual inspection has subjective criteria because it relies on the human eye. Today in industries, following technological evolution, the amount of automated systems is increasing and their accuracy is improving, especially with regard to repetitive and tedious processes. Under repetitive conditions, humans are especially susceptible to mistakes. In this sense, one of the growing tendencies is the use of machine vision, which seeks to have machines extract visual information, in other words, aims to make machines see, replacing the human visual system. With automated optical inspection systems, it is possible to maintain high performance and a low error rate and to analyse details that humans cannot perceive.
This work proposes the application of automated optical inspection techniques to extract quantitative information about surface corrosion status. Specifically, machines that conduct texture characteristic analyses for images are employed. Our aim is to demonstrate that for a given area, monitoring the changes in images over time makes it possible to show how corrosion evolves over time. The main contribution of this proposal is the possibility of using texture digital image analysis as a complementary tool for conventional corrosion monitoring techniques, for example, in the detection of the moment in which surface recovering must be done or metallic components must be replaced. Corrosion and automated optical inspection are old and well-developed research areas. The interface between these subjects is not recent either. Application of automated optical inspection to analyse corrosion was already proposed in the 1980s by Itzhak, and Dinstein Zilberberg [3]. However, the integration of these research areas still rarely appears in the literature, with only a slight increase in recent years. An overview of the literature on automated optical inspection for corrosion analysis is presented in table 1. The authors were classified according to their approaches. Although several authors propose specific applications, we grouped them into three categories. The first one includes approaches relating to analysing the corrosion evolution. The second addresses the recognition of the type of corrosion or if there is a principle of oxidation. The third category includes corrosion measurement based on some parameter or standard. Table 1. – Automatic optical inspection (AOI) approaches applied to corrosion analysis. AOI technique Boundary or shape analysis Image Correlation Texture Fractal geometry Another approaches Transforms
Application in corrosion Evolution analysis Recognition Measurement Evolution analysis Recognition Evolution analysis Recognition Evolution analysis Measurement Evolution analysis Recognition Measurement Recognition Measurement
Reference [3, 4] [5, 6, 7] [8, 9, 10, 11, 12] [13] [14] This approach [15] [16] [17, 18] [19] [20, 21, 22, 23] [24, 25, 26, 27, 28, 29] [30, 31, 32, 33] [34]
This work presents an atmospheric corrosion test conducted over 44 days to obtain a photo sequence of 24 samples obtained from 3 ASTM A36 steel specimens. Once these images were processed, time values for six textural characteristics were obtained. These values are presented in the graph "feature variation versus time". All test parameters and techniques are described below.
2. Texture analysis methods Automated optical inspection is a machine vision application used in inspection and quality control. It allows machines to extract visual information, replacing the human eye. With these systems, machines can analyse details that the eye and the human mind could not realize in quick analysis. The advantages of these vision systems are their accuracy and comprehensiveness, and they maintain high performance in various industrial and commercial activities. Texture analysis is one of the automated optical inspection approaches that extracts surface feature information. This technique is compatible with the evaluation of corrosion because the oxidation process starts at the surface of the material and changes its characteristics gradually over time.
“The texture is among the features used by the human visual system, containing information about spatial distribution and luminosity variation and describing the structural surface arrangement in relation to neighbouring regions” [35]. The texture is characterized by the image repetition in a given region. The textel (texture element) is defined as the basic building block of texture, i.e., the smallest digital image area that composes a distinct texture. The textel can be repeated on the image with variations in size, intensity, colour and orientation and still contain noise. The purpose of the texture analysis is to identify the neighbourhood of these similar elements that characterize the connectivity, density, and homogeneity. The human eye easily recognizes texture, however, to develop digital processes able to measure and describe texture is extremely complex. Various techniques can be found in the literature to identify texture features [35, 36]. Most of them are based on greyscale pixels. The most appropriate for each application should be defined based on the context because despite the existence of several methods, none are able to effectively target all types of texture. The application proposed here does not employ any technique to identify the texture directly. It uses texture measures that are expressed by numerical values and are able to quantify certain characteristics, such as entropy, Hurst coefficients, correlation, energy, or homogeneity, to observe how these characteristics evolve with corrosion. These characteristics and their methods of calculation are detailed below. 2.1. Entropy
Entropy is a statistical measure of randomness that can be used to characterize the texture of the input image and is defined as: entropy = ∑ · log
(1)
where contains each image histogram value. In a 256 greyscale image, i goes from 1 to 256. 2.2. Hurst coefficient
The Hurst coefficient is used as an approximation of the fractal dimension for images in grey levels. The fractal dimension can be used for determination of surface roughness, wear, erosion, corrosion and more. It is useful to characterize texture with numerical indices. The Hurst coefficient is calculated for a part of the image using the following steps: First consider the Euclidean distance d of each pixel i to the central pixel c. , ; ,
(2)
Then, the pixels are organized into n groups by Euclidean distances d. For each group, calculate the difference dif between the greatest grey level and the lowest grey level found. Calculate logarithmic coordinates ln , ln for each group. Finally, using the least squares method, set the straight line defined by the logarithmic coordinates. The coefficient a is the desired Hurst coefficient:
∑ ∑ ∆ ∑ ∆
∑ మ ∑ మ
(3)
2.3. Spatial co-occurrence texture features
In addition to the distribution, these measures take into account the pixels’ spatial relationship. The co-occurrence matrices are defined in such a way that each element of this matrix represents the frequency , at which a pixel with a grey level i and another with grey level j occur in an image
separated by a distance d in the theta direction. From these co-occurrence matrices, it is possible to calculate numerical values called descriptors that provide information about the original image; among them are probability, entropy, differences moments, energy, variance, correlation, homogeneity and others. In this research, only the following were used: Contrast. This is the measure of intensity contrast between a pixel and its neighbour. contrast ∑,| | ,
(4)
Correlation. This is the statistical measure of how correlated a pixel is to its neighbour over the whole image. Note that is the standard deviation and is the mean value of the elements. correlation ∑,
,
(5)
ೕ
Energy. This is the sum of squared elements in the co-occurrence matrix. energy ∑, ,
(6)
Homogeneity. This is the measure of closeness of the distribution of elements in the co-occurrence matrix. homogeneity ∑,
, | |
(7)
3. Materials and methods Several associations, societies and companies such as ASTM, NACE, ABNT and Petrobras (Brazilian Oil Company) define standards for testing, inspection and monitoring procedures. Those considered relevant to the tests and analyses used and proposed here are ASTM [37, 38, 39, 40] and NBR [41, 42, 43, 44]. Among these, ASTM G50 (40) and NBR 6209 (42) are specifically geared towards observing the performance of the test: the mass gain/loss and localized attack depth. As this work focuses on modifying the surface texture and the tests are non-destructive, there was no need to follow the parameters related to mass and attack depth measurements or the removal of the specimens. Exposure conditions such as inclination, surface orientation, absence of vegetation near the atmospheric panels and no contact with conductive material were kept exactly as prescribed in atmospheric test standards [40, 42]. For the same reason, other parameters, such as the specimen size, its surface preparation and degreasing, atmospheric environments, the number of test specimens required and the total exposure time had to be adapted as set out below. The specimens used were ASTM A36 carbon steel in free contact with the atmosphere, i.e., without any type of cathodic protection, passive layer or painting. These were obtained from 100x100 mm square pieces cut from 2-mm-thick plates as shown in figure 1. To ensure the original steel properties, discarding any pre-existing corrosion or encrustations that could be included on the surface, the specimens were prepared by removing a thin layer using a solid remover disc composed of synthetic fibre, resin, and silicon carbide. As the surface preparation was done immediately before the test started, degreasing was not necessary. After removing the surface layer, only clean dry airflow was used in preparation to start the test as in NBR 6210 [43]. The abrasive method associated with slow rotation is not able to create the conditions to change metallurgical characteristics; however, heat is generated in the cutting process. To disregard this effect, an edge of 15 mm around the specimen contour was left out of the results analysis, leaving only a useful 70x70 mm square region as shown in figure 1. For identification, small drilled holes were made in the edge. For orientation in later acquired images, one chamfer was left in the vertice.
Fig. 1. Specimen dimensions. 1- Chamfer to position mark. 2 - Identification holes. 3 – Useful area. 4 – Discarded edge.
The test site is an urban area (22º52'46" S and 42º01'07" W), approximately 1000 metres away from the sea waves formation line, in the city of Cabo Frio in Rio de Janeiro, figure 2. This place has both urban and marine atmospheres, little rain, 500 mm/year average pluviometry and high intensive NE and S-SW winds, both blowing from sea to land with 5.6 m/s average speed and reaching up to 10 m/s [45, 46]. The deposition of chlorides produced by marine aerosol is increased and has greater reach due to these high winds. This combination of factors contributes to corrosion.
Fig. 2. Test site, urban area of Cabo Frio/RJ, Brazil. Google [47]
In a harsh, especially adverse environment, lacking any corrosion protection, the ASTM A36 steel presents rapid modification in its features on the very first days of exposure. Exposed to rain and dew, the changes in the rust layer become abrupt. That’s why special attention has been paid: a covering was used to protect the specimens; thus, specimens were exposed to chlorides, atmospheric temperature and wind action but were free of any precipitation. A higher frequency was used for capturing images in the beginning, which was reduced over time, as in figure 3. For the first two days, three photos were taken per day; for the next three days, two per day; and from the sixth to the twelfth, one per day, From then on, photos were taken every three days and then every four days according to the following scheme. The result was twenty-four photos of each specimen over a 44day time interval.
Fig. 3. Timeline of photos taken over the 44 testing days. Higher frequency has been used in the beginning because there is rapid modification of the features on the first days of exposure.
For image acquisition, to avoid external lighting influences, one chamber light was used. Figure 4 shows this chamber design that includes LEDs for lighting control, camera and specimen positioning and other conditions to standardize images:
Fig. 4. Chamber to capture images under light control and position. AA) Lateral section; BB) Lid view; CC) Leds section; DD) Internal basis section; 1) Chamber Lid; 2) Camera side guide; 3) Camera; 4) Lid hole to fit the camera lens; 5) Antireflective shield; 6) Crystal LEDs; 7) Specimen side guide; 8) Specimen; 9) Chamber Base; 10) Fiducial marks; 11) Colour calibration.
Image registration is a geometrical transformation that relates the row and column coordinates between two images to eliminate existing misalignments. As the capture system is not able to ensure precise alignment, a step of image registration is required. For this reason, fiducial marks were included in the chamber. The photos were acquired by a digital colour camera with 14 megapixels. Due to shooting conditions, camera placement and 70x70 mm useful area for specimen analysis, the cropped photo (equivalent to the useful area) covers 3.06 Mpixels in a 1750x1750 pixel square region (653 dpi). For digital analysis, a 500x500 pixel greyscale sample was used (20x20 mm). Due the difference between specimen useful area and sample image, it is possible to extract eight samples from each specimen, for a total of 24 samples.
4. Results and Discussion Twenty-eight images were taken throughout the test from each of the 24 samples. Figure 5 shows only 10 images of the same sample. After processing, these images generated a numerical results value table for each textural characteristic considered. For example, table 2 shows part of the entropy values. Observe that the columns present the values of the texture features for each sample (A to H) obtained from 3 specimens (1 to 3). The lines show the 28 different instants in which the photos were taken. For each instant, the arithmetic mean and the standard deviation of the 24 samples were calculated and those results plotted in graphs (figure 6 to 11). Here the arithmetic mean for each instant is being represented by dots, while standard deviations by scatter bands.
Day 1
Day 2
Day 3
Day 5
Day 6
Day 8
Day 11
Day 15
Day 30
Day 44
Fig. 5. The atmospheric corrosion evolution in an ASTM A36 sample. Here, only 10 of the 28 images generated over the 44 days of testing are presented.
Table 2 – Summary of values for entropy. Columns present values for each sample (A to H) obtained from each specimen (1 to 3), lines present the test instants. Mean and standard deviation were used to generate the graphs.
28 taken images
Day 1, 7h Day 1, 15h Day 1, 23h Day 2, 7h Day 2, 15h ڭ Day 4, 19h Day 5, 7h Day 5, 19h Day 6, 7h Day 7, 7h ڭ Day 34, 7h Day 38, 7h Day 42, 7h Day 44, 7h
1-A 4,07 4,22 4,64 4,86 4,90 ڭ 6,60 6,73 6,89 7,00 7,10 ڭ 6,01 5,97 5,84 5,72
1-B 4,53 4,58 4,91 5,00 5,03 ڭ 6,54 6,73 6,83 6,94 6,99 ڭ 6,06 6,02 5,90 5,78
1-C 4,11 4,30 4,87 4,95 5,09 ڭ 6,76 6,94 7,05 7,15 7,16 ڭ 5,98 5,94 5,83 5,72
ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ
2-B 4,05 4,20 4,52 5,51 5,95 ڭ 7,08 7,17 7,17 7,22 7,16 ڭ 6,09 6,00 5,79 5,70
24 samples 2-C 4,03 4,25 4,61 5,59 6,06 ڭ 7,13 7,19 7,19 7,18 7,08 ڭ 6,09 6,01 5,80 5,70
2-D 4,33 4,46 4,65 5,44 5,82 ڭ 6,96 7,04 7,03 7,06 7,10 ڭ 6,00 5,97 5,78 5,71
ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ ڮ
3-F 3,83 3,85 4,38 5,46 5,75 ڭ 6,66 6,94 7,01 7,09 7,12 ڭ 5,94 5,65 5,56 5,50
3-G 4,77 4,74 5,01 5,75 5,97 ڭ 6,66 6,87 6,95 7,03 7,05 ڭ 6,01 5,78 5,71 5,62
3-H 4,94 4,96 5,21 5,96 6,19 ڭ 6,86 7,06 7,13 7,20 7,19 ڭ 6,15 5,90 5,81 5,76
Values for graph Mean StD 4,35 0,40 4,44 0,37 4,81 0,28 5,42 0,36 5,67 0,46 ڭ ڭ 6,84 0,25 6,99 0,19 7,06 0,16 7,12 0,14 7,14 0,13 ڭ ڭ 6,05 0,10 5,94 0,15 5,80 0,14 5,71 0,13
The texture features studied were Entropy, Hurst coefficient, Contrast, Correlation, Energy and Homogeneity. The last four were calculated from co-occurrence matrices. Thus, each was calculated for three different co-occurrence lengths: 2, 5 and 10 pixels. The values for each characteristic in a given co-occurrence length were based on the mean value for the co-occurrence in the vertical and horizontal direction. Entropy and Hurst coefficients were calculated directly from the sample image. Its behaviour over time is presented in figures 6 and 7.
Fig. 6. Entropy as a function of time (dots and scatters bands show mean and standard deviation for different sample values).
It is possible to observe that the entropy values form a set of points that present an over-time behaviour that can be understood as a curve. Additionally the values have a low standard deviation. This behaviour can in some ways be associated with visual changes in steel. Once the value of image entropy represents the randomness of the greyscale, the graph shows the increased randomness in accordance with the increase in the contrast between the amount of oxide on the surface (dark spots) and the metal (light areas). Around the 10th day, the oxide begins to cover the entire surface, thus, decreasing entropy. At the end of the test, it is clear that, although the oxide has taken the entire surface, it does not have a uniform colour, which contributes to the graph tendency to stabilize at an intermediate value.
Fig. 7. Hurst Coefficient as a function of time (dots and scatters bands show mean and standard deviation for different sample values).
The Hurst coefficient is based on a process that tries to obtain a numerical value to identify a shape or texture element in the picture, although a good feature to identify texture does not present a suitable behaviour for the approach presented here, as it presents a very varying mean and the standard deviation between samples is too high. This behaviour is driven by the corrosion evolution.
Such evolution does not follow a standard, like stripes or spheres. Instead, a pattern of random spots can be seen. For the four texture features studied, which were based on the co-occurrence matrices, the results also showed an interesting behaviour in figures 8, 9, 10 and 11. Like the entropy, they present a set of points that can be observed as a curve. When comparing the results for the three co-occurrence values studied in each feature, it is noted that the differences in distances of co-occurrence only result in differences in the features’ values; there is little variation in the behaviour of the curve points. This conclusion becomes more evident when observing the comparative graphs below.
Fig. 8. Contrast as a function of time. Specifically, 2-, 5- and 10-pixel co-occurrence length and comparative (dots and scatters bands show mean and standard deviation for different sample values).
The Contrast expresses the difference in intensity between each pixel and its neighbours. As it is in entropy, the bigger the quantity of dark spots, the higher the contrast value. When the dark spots reach the whole surface, the measured contrast no longer represents the metal-oxide difference, but the oxide-oxide difference.
Fig. 9. Correlation as a function of time. 2-, 5- and 10-pixel co-occurrence length and comparative (dots and scatters bands show mean and standard deviation for different sample values).
The correlation shows to what extent a pixel is correlated to their neighbours, that is, the greyscale similarity is what counts: the closer to the shade of grey, the higher the correlation value. Therefore, graphs now show an inverse behaviour. At first, the surface has uniform colour and high correlation. However, this value tends to decrease, depending on what the oxide spots look like. In other words, oxide predominance is what interferes in such correlation. Both in contrast and in correlation, graphs differ widely, according to the increase in the cooccurrence distance. This happens because the oxide spot occupy a group of pixels. When comparing a pixel inside the oxide spot to a neighbour being two pixels far, that neighbour is very likely to be in the same oxide spot. For a greater distance, the comparison is drawn by means of a pixel that is outside the oxide spot.
Fig. 10. Energy as a function of time. 2-, 5- and 10-pixel co-occurrence length and comparative (dots and scatters bands show mean and standard deviation for different sample values).
The matrix of co-occurrence presents major values when there is high frequency of the same combination of grey tones. When the oxide is dispersed in points, this frequency falls. The energy is the sum of the squares of the values in the co-occurrence matrix. So, the image being uniform, it will be high. Once we disconsider the neighbourhood, the distance of co-occurrence has little influence.
Fig. 11. Homogeneity as a function of time. 2-, 5- and 10-pixel co-occurrence length and comparative (dots and scatters bands show mean and standard deviation for different sample values).
Homogeneity and energy are analysed in much the same manner. However, the similarity in the repeated grey tones influences the result. This makes homogeneity and energy graphs resembling. However, the co-occurrence distance does influence more. All graphs show the curves have a maximum or a minimum around the 5th or 10th test day. In Figure 5, around this day, approximately half of the surface is covered by oxide. This feature results from the way the oxide scatters during this period. The method can demonstrate the metal surface conversion to oxide. The values taken as change indicators reach their extreme precisely in the middle of this transformation. These maximum and minimum episodes at the beginning of the test are somehow predictable, other corrosion monitoring methods, such as the corrosion rate obtained by the gain or loss in mass, also show a similar, more marked behaviour at the beginning. 4.1. Conclusions
The results show that texture analysis is feasible as a new method for checking the state of surface corrosion. According to this analysis, of the six characteristics studied, only the Hurst coefficient presents data varying in a way that does not behave as a curve that can be compared to the corrosion process evolution.
We aim to use these texture features to define the curves that express the corrosion evolution and therefore the surface degradation. When the curve for a material that is subject to a given environment is known, it will be possible to give an indication of the surface deterioration rate, from a simple photo, through comparison. Being visual, the method might be applicable for both occasional visual inspections and complex procedures for monitoring corrosion. Because it is done by computer, the technique allows more control, precision and it is not susceptible to human error. It is a non-destructive method, and it only requires a camera and a processor that is capable of performing relatively simple algorithms, what represents an inexpensive and not sophisticated alternative as is the case of the specimens in atmospheric panels. Once the control of the light and the correct positioning of the camera are ensured, the analysis of the structures outside the laboratory becomes possible, which eliminates the specimens needed. The technique is limited to examining the material surface conditions. It cannot, for example, identify the oxide layer thickness or the pitting depth. In some cases, this may mean a relatively generalized and relatively small corrosion. In case of abrupt changes in the corrosion rate, the method is not seen as efficient enough.
5. Acknowledgements This research was supported by the Universidade Federal Fluminense (UFF), the Instituto Federal de Educação Ciência e Tecnologia Fluminese (IFF) and the funding source was provided by Brazilian agency CAPES.
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Highlights
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Use of texture analysis is feasible for non-destructive surface corrosion monitoring Changes in image characteristics show how corrosion evolves over time We use entropy, Hurst coefficient, contrast, correlation, energy and homogeneity. Curves present behaviour that can be compared to the corrosion process evolution. The technique is feasible as a new method to check the surface corrosion state.