Data augmentation approach in detecting roof pathologies with UASs images

Abstract

Machine learning and computer vision techniques contribute to the automation roof pathologies identification from images collected with Unmanned Aerial System (UASs). However, one of the challenges for practical machine learning model tuning is the small-data problem. One strategy is to adopt data augmentation for generating more training data from existing images. This paper evaluates data augmentation in detecting pathologies in roof inspections with UASs images. The study adopted data augmentation for training two models in an image processing system. The training and tests using data augmentation images obtained superior results in accuracy, precision, recall, F-score, negative precision, and specificity metrics compared to the study using only original photos. These results indicate that data augmentation improves the adopted system's performance in identifying roof pathologies in UAS images. This inspection system proposed with such integrated technologies would make it possible to increase transparency, simplify steps and reduce the time to perform roof inspections, streamlining the preparation of reports and application of corrective actions.