Blobs and Cracks Detection on Plain Ceramic Tile Surface

Defects detection on ceramic tiles is a major issue in the ceramic tile industry to maintain the Quality of tiles. On the other hand maintaining the production rate with respect to time is also a major in the ceramic tile industry. Considering these criteria an efficient surface defects detection technique has proposed in this paper that can ensure better quality of tiles in the production process as well as production rate is also improved. Our proposed method plays an important role to maintain the quality standards in the ceramic tile industry. This proposed method detects the surface defects such as blobs and cracks on ceramic tiles in a very short period of time with high accuracy. In ceramic tile production process many stages lead to different types of faults and defects on the final product. These defects may occur due to chemical impurities in the material or due to some physical faults in the production process. At present the most of the phases of ceramic tile production are automated, but still the last phase of tile inspection is done manually. For inspection of ceramic tiles, the industry requires the human experts. Experts may have different opinions about the presence of defects. The capability of the human depends on training, knowledge and experience. The drawback to the manual inspection is that, human can work for a limited time and easily get tired within a few hours. The judgment of human is affected by fatigue. On the other hand the production process runs continuously in the factories, but inspection system doesn"t match with the rate of production. So, the need of automated system comes into the picture. The automated system is free from tiredness and the inspection is done with the same efficiency for all tiles. The blobs and cracks are common defects that we easily found on the ceramic tile surface. Considering all the problems related to tile inspection, an efficient method is developed for ceramic tile inspection, which can detect blobs and cracks very efficiently on the surface of ceramic tiles. The proposed method is based on image processing and morphological operations. The objective of the research paper is to propose an efficient defect detection technique which can find out surface defects on images of tiles with high accuracy and within a very short time. The proposed paper is organized as, section 2 represents literature review. In section 3 gives a brief overview of techniques used in proposed method. Section 4 provides the explanation of the proposed approach. In section 5 and section 6 experimental results and conclusion is mentioned. II. Related Work Throughout the last decade many kinds of defect detection systems have been developed. These systems have developed to identify the various kinds of defects on ceramic tiles and applied to industrial process. D.O. Aborisade (3) has suggested a method for automatic surface inspection of plain ceramic wall tile, which is based on computer vision. The algorithm starts with an application of edge enhancement operation to input image in order to highlight the edges of the objects in the images of tiles. Image edge enhancement and detection algorithms are applied using a spatial convolution process. A 3X3 mask is used to measure the gradient and the direction of the gradient in the image. The gradient at a specific location is threshold to obtain the edge map of the defect. After segmentation of cracks from the image some discriminant functions were developed. To identify the tiles into either a defect class or a reject class based on the feature extracted at the real time. In H. Elbehiery, A. Hefnawy, and M. Elbehiery have proposed an algorithm for surface defect detection on ceramic tiles which is based on Image processing and morphological techniques (4). First part of the algorithm starts with image acquisition, and then histogram equalization is performed on the images. The intensity adjustable histogram equalized images are the input to the second part of the algorithm. The second part of the main algorithm includes many of the individual complementary algorithms differ due to the various kinds of defects. For cracks and long crack detection the input images are converted to black/white images. An edge detection operation is performed to detect the defect pixels. Some morphological operations have been done to discriminate the defect pixels more accurately followed by noise reduction and smoothing object processing to get clear image containing the only defect. Pinholes are detected using grayscale morphological operations followed by noise reduction processing to get a clear image for the defects. Similar approach is used for blob detection. Their algorithm requires much computational time because for each type of defect the