Crack detection in buildings using the YOLO v8 network

Abstract

The objective of this study is to develop and apply deep neural networks for the automation of crack detection in buildings. The methodology involved training the YOLO v8 network with images collected from the internet, aiming to identify and locate cracks in real time. The model obtained 80% accuracy in validation with images not used in training, despite performance limitations in Google Collab. These limitations included restrictions on the execution environment, and the model is specific to cracks. The originality of the tool lies in its relevance for the automated detection of cracks, with the potential to extend to other pathological manifestations. It is concluded that the application of deep neural networks offers an efficient solution for the identification of problems in buildings.