Ensembles of Deep Neural Networks for the Automatic Detection of Building Facade Defects From Images

Preserving the value of buildings and ensuring performance levels within acceptable parameters throughout their lifespan necessitates constant monitoring. In recent years, artificial intelligence has provided a valuable supplement to conventional inspection practices, potentially offering a supporting tool for building maintenance in smart cities. Exploiting machine learning algorithms for detecting or classifying building facade defects from acquired images has emerged as a promising automatic building monitoring strategy. However, an effective approach should be capable of accurately classifying fine-grained defects, thus requiring ad-hoc solutions to maximize predictive accuracy. For this reason, in this work, we introduced a novel and effective classification protocol, based on different ensemble strategies of complex and recent deep neural networks, namely Vision Transformers and ConvNexts, for building facade defects automatic classification. First, we validated our method on a popular benchmark dataset with different damage classification tasks, outperforming the state-of-the-art available works. Then, we analyzed a custom dataset, named Facade Building Defects (FBD), containing building facade images labeled into four different defect classes, that we introduced in this work and released as open access. The proposed ensemble showed a test accuracy of 90.9%, achieving an improvement of 1.6% with respect to the best single model, thus empirically proving the benefit of model ensembling for the task of automatic building facade defects classification.