AUTOMATED TILES CRACK DETECTIO…

AUTOMATED TILES CRACK DETECTION SYSTEM USING IMAGE PROCESSINGINTRODUCTION The stability of structure is an important factor not only in the construction phase but also in the maintenance phase.The importance of safety in facility is increased as the construction of high-rise buildings, super long span bridges, and asymmetric buildings are popular. For this reason, construction safety management system is actively under development these days. In addition, the interest in the automation of the construction management system has been increased, because the civil infrastructure under the management is large in scale, the regular evaluations are needed to guarantee the continuity in service, running cost is very expensive, and the safety of workers should be ensured. By developing an effective infrastructure lifecycle management system through automation, it is possible to secure the stability of the facility, and reduce the number of inspectors, inspection, time, and maintenance cost. Moreover, we can judge the condition of the structural health objectively by acquiring and processing the data.Most of the infrastructures are composed of ceramic tiles and concrete. In these structures, the one of the ways in judging the structural health is to examine a crack on the surface of the structure. Since the condition of a ceramic tiles structure can be easily and directly identified by inspecting the surface crack, the crack assessment should be done on a regular basis to ensure durability and safety within its life-cycle.Using human visual inspection is the oldest and most reliable method to recognize tiles cracks. However, using human inspectors is time consuming, expensive, and can pose risks to human safety. The Proposed Automated Tiles Crack Detection System Using Image Processing methods have been on the rise for the past decade, letting the inspection be done in a more efficient manner. This techniques is obtained using High Digital mobile Camera to capture, Detect cracks and other defect on tiles using Matlab.Each image captured by the Camera needs to be evaluated to track the crack formations. To save time, this task can be done by applying image processing techniques to automatically detect and report cracks rather than using a human to identify them. In addition, processing RGB images with sufficient information, such as the distance of camera to surface for each picture, will provide the Dimension of the cracks (length and width).The crack detection in this report was built on the work of Matlab with modifications. RGB images are captured from Tiles surfaces using a 16-megapixel digital camera Phone. Image processing is one of the mostly increasing areas in Computer science. As technology advances, the analog imaging is switched to the digital system now-a-days. MATERIALS AND METHODSThe proposed method provides a simple and fast automatic crack detection algorithm on tiles using image Processing Techniques. The input of the proposed method is any RGB image with ‘jpg’ extension of any size. However, the program can be easily modified for any other extensions. The proposed algorithm has been written in Matlab 2015 software.Material Needed1. Two Camera: to capture the image of the crack tiles.2. The Prewitt Edge Detector: this operator is used in image processing, particularly within edge detection algorithms. Technically, it is a discrete differentiation operator, computing an approximation of the gradient of the image intensity function.3. Matlab 2015: A software programming language that be used to analysis the capture image to detect the crack.STEPS OF THE PROPOSED METHOD The following steps are required to find cracks in the proposed method: Step 1. Image AcquisitionThe images of tiles are acquired using a digital Phone camera. A distance of 1.5 meters is maintained between the tile surface and the camera lens to ignore the texture of the tiles in the images captured by camera. For experimental purposes, a 16 mega-pixel digital camera is used to capture the images of tiles. Step 2. Contrast Enhancement Many times the images taken are very dark due to Poor illumination, lack of dynamic range of the imaging sensor or due to wrong setting of the lens. Using contrast stretching, the contrast of the pixels of the image is enhanced to obtain an image with an enhanced contrast which represents an appropriate and reliable image for feature extraction. Contrast stretching increase the dynamic range of the intensity level in the processed image. The proposed method used Adaptive histogram equalization technique for contrast enhancement of the images. Adaptive histogram equalization divides the image into multiple local regions, and then histogram equalization is performed in each local region. It is the most common technique for contrast enhancement in the images. The poor contrast images don’t provide good results because it becomes difficult to identify the difference in the intensities of defects and background surface of tile. After contrast enhancement the images of tile become ready for the further processing steps of the processing approach. Step3 .Noise Removal Noise it the disturbance created in the image, it may be due to low contrast, movement of the camera on the object and wrong setting of camera lens etc. The noise increases the some unwanted pixels in the image. This leads to false detection of region of-interest in the image. Image filtering operations are performed on the images to suppress the noise in the images. In our proposed method we use median filter and wiener filter to reduce the effect of noise in the images. The median filter is an effective technique that can suppress isolated noise without blurring the sharp edges. Edge Detection Specifically, the median filter replaces a pixel by the median of all pixels in the neighborhoodStep 4. Edge Detection Edge detection is the most common approach for detection of meaningful discontinuities in intensity values occurring in the image. There are many ways to perform edge detection. However, the most edge detection techniques can be classified into two groups, Laplacian and gradient. The Laplacian technique uses a second order derivative of the image to search zero crossing in the image for edge detection. On the other hand the gradient method detects the edges by looking the maximum and minimum in the first order derivative in the image. This will be achieved by using two cameras on the sides of the tiles to inspect the edge of the tiles while a camera on top investigates the surface. This strategy would control surface and edge inspection in ceramic tiles.In the next stage, we recommend using histogram subtraction to separate the tile from background. This were the application of canny edge detector will be usefull because i want to focus on line gradient and this method is the one suitable for this purpose (Atiqur Rahaman ; Mobarak Hossain, 2009). Finding a suitable threshold in canny technique will have a great effect on the work to succeed. In addition, using morphological operand (opening, closing) would help to remove unwanted noises (Ze-Feng, Zhou-Ping, ; You-Lun, 2006).The Laplacian technique uses a second order derivative of the image to search zero crossing in the image for edge detection. On the other hand the gradient method detects the edges by looking the maximum and minimum in the first order derivative in the image. Edge detection techniques have four common steps 1. Smoothing: suppressing the noise to the maximum extent without disturbing the true edges. 2. Enhancement: applying a filtering operation to enhance the edge quality in the image. 3. Detection: identifying which pixels should be retained as edge pixels and which should be discarded. 4. Localization: Identifying the exact location of an edge in the image. This step requires edge thinning and linking operations. In the proposed method Prewitt edge detector is used for edge detection. Step5. Segmentation Thresholding is an intensity-based segmentation technique in which a threshold value is selected and the object of interest is extracted from a background. A threshold value is selected to differentiate the pixels into two groups, one having an intensity level lower than threshold value and another group have pixels those have higher intensity value than the threshold. If a single threshold value is applied to an image, it is known as global thresholding and if a threshold value depends on the neighboring pixels and varies accordingly, it is known as local thresholding. The expression defines a thresholding operation. Step 6. Morphological operations Morphological operations are very useful to remove the unwanted pixels from the image. Morphological operations take a binary image and a structuring element as input and perform the set operation such as intersection, union, complement and inclusion to produce the output results.The complete flow chart of the proposed tile crack detection and classification technique. Flow chart of the proposed tile crack detection SystemProposed approach diagram for edges of ceramic tiles Image processing step including parameter optimization architecture Fig 5. Tile passing through different image processing techniques and morphological operation. Advantages: 1. The Proposed method is able to detect the crack defects more accurately and efficiently within a very less time. 2. The Proposed method is able to identify defects like Blobs, Cracks and even Pin Holes on a plain ceramic tile surface. 3. The proposed method is able to identify the crack length, thickness and witness The proposed method is also able to reject the defective tile.