Main Article Content
Detection of crack on concrete is very important for the renovation of concrete structures. Besides, little cracks that seem immaterial ought to develop and sometimes cause extraordinary basic disappointment. This issue identifies with the basic wellbeing and unwavering quality, consequently, it should be settled as ahead of schedule as plausible to stay away from additional loss. Manual review needs objectivity inside the quantitative investigation. Converting an image from RGB to grayscale, image filtering with Gaussian filter, subtraction, blurring, image segmentation, edge detection, etc. many activities of image processing with neural Network. The algorithm is composed of two parts. The First is image processing with neural networks and the second is image classification technique using machine learning algorithms. First Remove the background using filtering and improved subtraction method and morphological operation and then edge detection. Then the next step is identifying the crack and non-crack image. And if the crack image identifies. The purpose algorithm verifies the two-step tested with backpropagation neural network Thus, we tend to expand the version to identify the existence of cracks and we that teach the version to categorize the images using naïve Bayes, logistic regression, and Support Vector Machine type algorithms and decide the fine model to classify concrete photographs as crack and non-crack.