Crack detection system in AWS Cloud using Convolutional neural networks

In the time on structured surfaces (walls, roofs, bridges, streets, etc.) cracks appear and influence from the aesthetic point of view, but also from the point of view of their resistance and quality. Traditionally, crack detection is performed by human visual inspection, which is dangerous (when they need to climb buildings), subjective (depending on their experience in detecting the severity of a crack), and time-consuming (if we consider hundreds of buildings). Increasingly, artificial intelligence continues to evolve and we can use it to improve human performance and automate the process of crack detection. In order to improve this problem, we present an application that detects cracks in buildings that are difficult to access or would endanger human life. The architecture of our application is based on Convolutional Neural Network. In this paper, three different approaches are described and compared.