The work is devoted to the problem of identification and quantitative estimation of pitting corrosion. The purpose of the work is to design an algorithm and create a set of programs for evaluating and predicting corrosion processes development on the aluminum surface. Object of a research is process of corrosion of aluminum with hydrogen depolarization. Results of work can be used for a research of corrosion processes and their mechanisms on the basis of the visual analysis.
One of the reasons of decrease in durability and loss of metal designs operability is corrosion destruction of metal. Most of all iron, its alloys and also aluminum suffer from corrosion. Metal designs are subject to generally electrochemical corrosion which arises at contact of metals with electrolytes. Diagnostics and forecasting of corrosion allow estimating service conditions of devices, machines and the equipment. Also they allow defining efficiency of methods and means of protection, to make the decision on their improvement.
Among the non-destructive testing methods for corrosion monitoring [
The most part of the works connected with detection of corrosion effects by methods of computer
vision analyzes cracks and pittings. In most of works, the problem of automation detection and defects
measurement was solve successfully. Studying of pitting corrosion of stainless steel in FeCl3 solution
became one of the initial experience with computer vision for the metals corrosion research [
The study of corrosion processes using computer vision methods involves four interconnected processes that create the prerequisites for the creation of data-processing system:
1. Accumulation of experimental data (corroded surface images), which require ordering, structuring and classification for correct use when solving the problem of classifier training.
2. Development of information processing methods depending on the type of problem being solved. In this case, it is the use of image processing methods, mathematical statistics and fractal analysis to evaluate and predict corrosion processes for various types of metals (steel, iron and aluminum).
3. Updating the technical means of processing information, increasing the calculations speed (using graphics processors).
4. Implementation of automatic systems of scientific research in the form of software packages and training of the end user (researcher).
In this paper, we study the results obtained with the help of the created information-computational analytical system (ICAS) for estimating and predicting corrosion (“CorOcenka”). The main results based on the use of ICAS are listed.
The image of an aluminum surface under corrosion potential in a 0.1 M NaCl solution (pH 11) is shown in figure 1. Process is followed by intensive release of hydrogen. Bubbles of the evolving hydrogen are presented on the image. The task of searching objects in the image was reduced to detection of hydrogen bubbles being evolved. The intensity of this evolution had to be determined from a series of pictures. On the volume of the evolved hydrogen the rate of cathodic reaction is estimated. This rate is equal to the rate of anode reaction and dissolution or corrosion rate.
In the study of the corrosion reactions such methods of information processing as image processing, fractal analysis, contour analysis and template matching are used.
Mathematical processing of experimental data consists in work with the image f which is matrix f(M,N): f (x, y) = f (M − 1, 0) f (0, 0) f (1, 0) f (0,1) K f (1,1) K
M M f (M − 1,1) K
M f (1, N − 1) f (0, N − 1) f (M − 1, N − 1) This matrix is mathematical model of the image. Each element of the matrix is image pixel. The following steps can be distinguished in the recognition of surface defects: 1. Image pre-processing (anti-aliasing, noise filtering, increasing contrast); 2. Actual processing (image binarization and isolation of object contours); 3. Image post-processing (filtration of contours on perimeter and area, on form coefficient and fractality, etc).
These operations are specific to each problem of computer visioin. Development and use of the
models suitable for the effective solution of a object detection and recognition problems, substantially
remains on the verge of science and art. The solution of this problem requires a special know-how or,
in other words, knowledge of subject domain reflecting long-term experience of a research on the
solution of specific tasks. In more detail this task is considered in work [
For search of a bubble on the image we will use the Histogram of Oriented Gradients method (HOG, [19]) The method consists in assessment of normalized gradients histograms of the image brightness. The image is divided into small rectangular regions, in each of which brightness gradient is calculated. For all received gradients in the area the histogram is calculated. The histogram is the descriptor of the image region. For increase in accuracy local histograms use normalization by contrast. In order to take into account changes in illumination and contrast, the strength of the gradient must be locally normalized, which requires grouping the cells into larger, spatially related blocks. Because of its locality, the HOG method is stable to rotations.
We will search for a "bubble" in the image using a "sliding window" with a size of 128 × 128 pixels. We divide it into blocks of 8 × 8. Then one window with a size of 128 × 128 pixels will contain 256 blocks. In each block, we calculate the histogram of oriented gradients with 8 cells. Thus, we get 8 × 256 = 2048 characteristics. In Fig. 2 the visualization of this method is presented, the bubble image has a size of 128 × 128 pixels and is divided into 256 blocks. For illustrative purposes in this case a scaled model is presented. Such a number of characteristics cannot be taken into account without the use of machine learning. An effective method for solving this problem is the support vector machine [20].
Machine learning is the extraction of knowledge from data. This is a scientific field combining the methods of statistics, artificial intelligence and computer science and also known as forecasting analytics or statistical training. The solution of the problem of binary classification using the support vector method consists in finding a linear function that correctly divides the data set into two classes. Consider the classification problem, where the number of classes is two.
The problem can be formulated as a search for a function y(x) that takes values greater than zero for vectors of the same class (for bubble images) and less than zero for vectors of another class (background). As the initial data for solving the problem, that is, searching for the classifying function y(x), a training set of space vectors is given for which they are known to belong to one of the classes. The collection of classifying functions can be described in terms of the function y(x). The hyperplane is defined by the vector a and the value b, that is, y(x) = ax + b. As a result of the SVM-model construction, a function that takes values less than zero for vectors of one class and greater than zero for vectors of another class is found. For each new object, a negative or positive value determines whether the object belongs to one of the classes.
The sequence of actions for training of the linear SVM qualifier consists of the following steps (Fig. 3): • We will analyze the image by means of a window of 128*128 pixels in size • We will break it into blocks of 8*8 in size. Then one window of 128*128 pixels in size will contain 256 blocks.
• We will count the histogram of oriented gradients with 8 cells in each block. • We receive 8*256 = 2048 properties шn total.
After the objects in the image are found, in this case a bubble, we calculate its area. Since the contour of the bubble is a circle, then, using the method of least squares for the circle, we can calculate the necessary values for the radius and center of the bubble.
The software package that implements ICAS “CorOcenka” was written in C ++ language in the Qt SDK environment using the OpenCV library, and SQLite was used as a database.
To solve the problem of corrosion analysis on the surface of aluminum, a sample was created, consisting of one hundred positive and negative examples.
On the sample, the SVM linear classifier was trained. Fig. 4 demonstrates that the classifier unsatisfactorily processes bubbles that partially fall into the frame or have small dimensions. This effect can be neglected to obtain an overall picture of the change in the area of the observed bubbles.However, to obtain an overall picture of the change in the area of the observed bubbles, this effect can be neglected.
To verify the performance of the classifier, its work was tested on a set of data from another experiment occurring under different conditions. The result of processing the experimental data is shown in Fig. 5. Despite the fact that the classifier was trained on the data of the first experiment, it showed good performance on an alternative data set.
The use of the parallel implementation of the SVM training had an excellent effect on the calculation time. In general, for the task of identifying bubbles on the image, the acceleration was more than 15 times.
The change in the total area of the bubbles as a function of time was obtained (Fig. 6).
Also the possibility of bubbles search by means of contour analysis is realized, but this operation
requires the user to introduce the parameters of binarization, noise reduction, morphological analysis
and others (Fig. 7). The implementation of this approach is considered in the paper [
The paper considers the main aspects of image processing and analysis in the studying the corrosion damage mechanism:
• The computer vision and image processing in the problem of detecting bubbles on the aluminum surface are developed; • The general regularity of corrosion data processing on a aluminum surface is defined; • The described algorithms are used for detection and recognition of corrosion damage for an aluminum sample by observing the intensity of hydrogen bubbles formation;
• The trained classifier was successfully applied to study several experiments of corrosion on the aluminum surface.