Priya Ranjan Muduli et al. (2013) identified a crack detection method in which they combined two methods and the methods were hyperbolic tangent filtering and Canny edge detection algorithm. They also used the Haar Discrete Wavelet Transform Algorithm (HDWT) where the algorithm decomposed a signal into two sub-signals. One secondary signal was the average and the other was the difference. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essayE. Balasubramanian et al. (2015) studied a new approach combining both HSV thresholding technique and hat transform for crack detection, and crack parameters were also obtained. Having overcome the difficulties of previous researchers, they proposed the new method. In HSV the object can be extracted by appropriately setting the bounds in the HSV algorithm. Using simple grayscale thresholding the assessment of surface degradation could be performed and was used to classify regions that are not affected. Arun Mohan and Sumathi Poobal et al. (2016) studied various crack detection methods. In their article they did an analysis based on various parameters. This analysis was based on objectives, techniques, level of accuracy, level of error and dataset. They did not address the different classification techniques and related issues. Rizvi Aliza Raza et al. (2017) proposed a new method for crack detection on railway tracks. They installed video cameras in different areas of the track for image acquisition and different methods were used for crack detection and the methods were de-noising, histogram equalization, morphological operation etc. Roberto Medina et al. (2017) used Gabor filters for the rotation method and their method was used to show different orientations along its length for single crack. Their method was insensitive to brightness. Haiming Liu et al. (2016) analyzed a method for detecting cracks in plastic surfaces. Plastic surfaces may have line-like cracks. They applied the reduction method for noise removal, image gradient for crack image reconstruction; the optimal method based on the crack shape was used in their research. The proposed approach was better than Otsu method and clustering method. Aliza et al. (2017) studied a crack detection method for aeronautical or automotive applications. In the case of aeronautical and automotive applications, it was not possible to detect cracks from a single image and it also took more time. Then they used methods like morphological operations, thresholding, Canny edge detection etc. Mojtaba et al. (2016) used some pre-processing techniques where they eliminate the impact of non-uniform background and road signs. They also used morphological operations to improve crack characteristics. Romulo et al. (2016) used a feature extraction method in their research. They used feature extraction method to differentiate outdoor images. Their method involved classification for window segmentation by color, crack detection for clustering, particle selection for particle filtering, and classification by direction with quantitative analysis for the least squares method. Rabihamhaz et al. (2016) used three methods for crack detection in their research. The three methods were Minimal Path Selection used..