1 ? Abstract — Most of industries around us make use of iron machines and tools for manufacturing their products. On the other hand corrosion is a natural process that deteriorates the integrity of iron surface. Therefore rusting of iron take place.
To avoid this it is necessary to detect rusting in earlier stage, so that it can be prevented. Digital image processing for the detection of the rusting provides fast, accurate and objective results. Thi s paper provides a brief description of the past and present technologies for rust detection. The proposed method attempted to create a program that is capable of detecting rust through image processing. Image processing is known for the manipulation of i mage through quantizing the image itself in matrix form. Through this quantization, it gives opportunity to not only manipulate the image but also detect a particular subject on the image as well, such as rust. Through setting the threshold values and the use of edge detection and segmentation, rusts on the image can be detected. The threshold values will set the parameters and characterize what a rust is.
The edge detection will check for the sudden changes of colors in the images. The segmentation will th en determine the colors on the image. The results in the edge detection and segmentation will be integrated to determine the rust on the image. Keywords -— Segmentation;thresholding;edge detection; MATLAB 1.
INTRODUCTION Iron machines and materials are used in most of industries for manufacturing products. In industries these iron materials come in contact with humidity and pollution, therefore increases the rusting of iron. Corrosion takes place when the mechanical materials come in contact with humidit y and pollution in industries.
Due to the attack of the corrosion, these mechanical materials undergo the fatigue that affects the integrity of the metallic surfaces. This rusting caused by corrosion causes wastage of iron materials, reduction in eff icienc y and costly maintenance . Different departments make use of materials that are made up of iron. In Civil department, for maintaining the good quality of steel bridges, it is important to detect rust defects in advance. By detecting rust defects in advan ce, bridge managers can make important decisions whether to paint bridges immediately or later.
Electricity department makes use of crossarms that are made up of iron. These crossarms undergo the process of rusting. Depending on the rust present on these c rossarms, the decisions are made by electricity department whether to reuse these crossarms or not. For making such kind of decisions of classification Support Vector Machine plays an important role .
To detect the rusting on the metal surfaces of aircraft s, texture analysis using image processing is done . To use digital image processing for the detection of rusting of metals is fast, convenient, a ccurate and very much objective . In this pap er, object detection will be used in detecting rust. The process requires segmented images of the material to be processed in MATLAB for rust detection.
Advantages of using image processing are the accuracy of reading, cost effective, faster, objective and consistent. 2. COMM ON STEPS FOR RUST DETECTION General block diagram for rust detection as shown in fig1.
General steps for rust detection include, A. Automatic capturing of images of materials using digital camera : A Digital Camera is used for automatically taking the imag es of the iron made materials. These captured images are then processed for the detection of the rust. It is to be ensured in these images whether there is presence of rust or not. B. Apply rust detection techn ique to ensure presence of rust: In this step t he captured images are processed to ensure whether they contain rust or not. This is the most important step for the detection of the rust. There are different types of rust detection techniques.
Each rust detection technique has its own series of steps fo r ensuring the presence of rust. When rust detection technique is applied on the captured images then it is ensured whether these images contain rust or not. RUST DETECTION USING IMAGE PROCESSING: A REVIEW DHANYA P , M.TECH Signal Processing , College Of Engineering Thalassery , [email protected] Dr. RINI JONES S B , Associate Professor & HEAD OF ECE DEPARTMENT , College Of Engineering Thalassery2 C. If image is rusted the n calculating total area to be rusted:: In this step of detecting rust in i mages, when images are found rusted then the total area that i s found rusted is calculated . This step is generally performed to make sure that the images is partially rusted or totally rusted.
Depending on the area found rusted important decisions are to b e made. Therefore, calculation of total rusted area is done. D. To make decision on the basis of total rusted area : In this step decision is made depending on the area that is found rusted in the previous step. Fig 1.
General steps for rust detection 3. PREVIOUS WORKS FOR RUST DETECTION Rust defect assessment is important in order to maintain a good quality of steel bridge painting. Bridge managers can more realistically develop long -term cost -effective maintenance programs if they have depen dable coating condition data. Also, they can make decisions as to whether a bridge shall be painted again immediately or later. Taking digital images of the steel bridge surface with a conventional camera to evaluate its painting surfaces offers the advant ages of being inexpensive, accurate, objective, fast, and consistent.
The proposed algorithm calculates the percentage of rust rather than just classifying an image as defective and non defective. In “wavelet domain detection of rust in steel bridge images ” proposed by Sindhu Ghanta and Tanja krap 1 is based on the concept of wavelet transform. This technique provides entropy minimization for illumination correction in the images. This is done as a preprocessing step for completely eliminating shading effects. In this technique directly colored image s are processed, therefore, there is no loss of information . The algorithm for detecting the rust defects has three steps that are Feature Vectors Extraction, Training and Detection.
Extraction of Feature Vectors, Training and Detection In this technique two feature vectors are used for classification: entropy and energy. After applying one level of wavelet transform to all the three color planes (RGB) of the image, the entropy and energy values are calculated in each sub band. By using the feature vectors extracted in this way it is ensured whether the image is rusted or not. Detection of Steel Defect Using the Image Processing Algorithms proposed by M. Sharifzadeh &S. Sadri; detection and classification of steel surface defects were investigated 2 . Imag e processing algorithms are applied for detecting four popular kind of steel defects, i.
e., hole, scratch, Coil break and rust. A set of 250 steel defect images were used for testing. Some of common operation for defect detectingare thresholding, noise re moval, edge detection and segmentation. thresholding is the first step for hole and scratch detection.
The second step is hough transform. Experimental results shows that hough transform of the holedefect has Gaussian function with large ? and scratch defec t has a small ?. For coil break detetion pixelshave been distributed over the wide range of steel sheet. Experimental result shows an evident difference between the histogram of this defect image and the other defects.
In this method the first step findin g the rust defects is segmentation. For segmentation, image has been thresholded. For thresholding, many methods such as Maximum Entropy Sum Method, Entropic Correlation Method and Renyi Entropy are reported . However, in this research Renyi Entropy is use d. In a study conducted by Huwang, N., Son, H., Kim, C., & Kim, a rust detection program was created to detect rust and determine the on which area the robot is going to do the grit – blasting procedure.
The first step in their program is the conversion of the RGB colors to HIS 3 . This procedure was done to eliminate the probability of false reading. After such, the image of the rust will then undergo to the process of classification, to determine what technique or process to be used in analyzing the rust.
The study offered six categories of techniques. The purpose of these techniques is to classify whether the pixel belongs to the background or the rusted area. In these techniques, the neighboring pixels were also checked for comparison on whether is a rus t or part of the background. Although, this study still needs further testing on its rust detection part, since its more on a comparative study.3 Towards Corrosion Detection System proposed by B.B.
Zaidan etc; which introduced a method for rust detection with the concept of texture analysis 4 . Texture analysis plays an important role in detecting the isolated data aand reducing the error and improving the classification results. The method uses texture segmentation with the aid of edge detection. Edge de tection is the process of finding sharp contrast in intensities in an image. This process significantly reduces the amount of data in the image while preserving the most important structured feature of that image.
The proposed solution has focused on the r ough texture of the corrosion areas, and identifies the simple texture as non – corrosion area. The test has shows a good result in term of detecting visible corrosion. Many studies has been done to tackle image processing based rust detection such as the s tudy done by Sharma V. & Tejinder T.,the techniques used for rust detection were discussed. The first step in doing rust detection using image processing is through obtaining the data, which is obtaining the image of the object. In this study the data was obtained automatically through a camera fixed on the object .
that is being monitored. The next step proposed is the detection of rust. In this step it was proposed that different rust detection techniques should be done, which might be due to different typ es and levels of rust. It was emphasized that different techniques have different steps to follow. The third step is calculating the area of rust on the image. This is to determine whether the object is either partially rusted or totally rusted.
An additio nal feature was added in their study, which to determine on what maintenance should be done to the object 5 4. PROPOSED MTECH PROJECT This method is aimed to create a rust detection program with a 90% success rate. It is implemented by using MATLAB. To execute the whole program, three functions were created, namely the thresholding, edge detection , and segmentation. Segmentation is the process of dividing the image into pieces.
Thresholding is the process if converting theimage into binary. The assigning of binary bits to the matrix of image is depend on the intensity of the background in the image. Edge detection is the process of recognizing the boundary of an object in the image processing. The output of thresholding was set to be the input of edge det ection function.
Edge detection works through checking the neighboring process if their range of value within the acceptable values in their cluster. These methods set the parameters of a rust and detects them through their matrix values. Block diagram of rust detection as shown in fig 2. The rust detection method used is based on image segmentation and image thresholding. The image are segregated into red, green, and blue channels.
The red channel image is stored into a 2D matrix. The grayscale of the image is then acquired. Thresholding is then applied to the image. The thresholding method used is Shannon entropy method. From the method, the threshold value is acquired and is used to binarize the image. After the binarization, the images are classified into non -rust images and rust images.
For rust images, the black pixel represents the rusted area and by computing the number of pixels and dividing it to the total number of pixels, the approximate percentage of rust is detected.The result of the program yields a 90% success rate in detecting rust on images and 100% in detecting non -rust images. Fig 2.
Block diagram for rust detection 5. ONCLUSION This paper described about rust detection techniques to classify rusted and non rusted images. In this paper we made a comparison analysis on different existing rust detection techniques and methods for classification of rusted and non – rusted images. So we can say that this paper can help those researchers who are planning to research in this field . T he proposed method can be implemented using MATLAB.4 This proposed method yield a success rate 90% in detecting rust on image with rusts and did not obtain any errors on images with no rust. REFERENCE 1″Wavelet domain detection of rust in steel bridge images,” In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
2 M. Sharifzadeh, S. Alirezaee, R. Amirfattahi and S.
Sadri, “Detection of steel defect using the im age processing algorithms,” 2008 IEEE International Multitopic Conference, Karachi, 2008, pp. 125 -127. 3 N. Huwang, H. Son, C. Kim, and *C. Kim, “Rust Surface Area Determination of Steel Bridge Component for Robotic Grit -Blast Machine,” In 2013 Proceedin gs of the 30th ISARC, Montréal, 2013, pp 1148 -1156.
4 B. B. Zaidan, A. A. Zaidan, H. O. Alanazi, and R.
Alnaqeib, “Towards Corrosion Detection System,” International Journal of Computer Science Issues, vol. 7, pp. 33 -36, 2010 5 A. Sharma, and T. Tejin der, “Techniques for Detection of Rusting of Metals using Image Processing: A Survey,” International Journal of Emerging Science and Engineering, vol.
1, 2013, pp 60 -62 .5A NON REFERENCE BASED IMAGE QUALITY PREDICTOR USING DEEP CONVOLUTIONAL NEURAL NETWORK:A REVIEW Page 6
