Public Database of Cracks Images in Mortar Coating with Different Types of Surface Finishes

Abstract

The use of technology, such as artificial intelligence (AI), in production processes has been optimizing several industrial realities. In civil construction, AI can be used in different applications, one of which is building inspection. One of the difficulties in developing this type of study is the low number of public image databases that represent more general aspects of building wear. In view of this, the main objective of this research was to set up a public database of images of cracks in mortar coating, considering different types of surface finish—smooth type, scrapped type, and rough type. A database was created with 33,088 images that went through a systematic labeling process based on classes defined in the study. Network training was carried out through transfer learning using the VGG16 in different groupings of finishes. It was found that the training accuracy varies according to surface finish and data balancing. The finish of the scrapped type was the one that presented the lowest accuracy. The database presented several types of noise and was unbalanced in all categories defined in the labeling. In this way, it was possible to create a database that represented possible situations to be found in real inspections.