Deep Learning for Detecting Building Defects

This review article identifies the deep learning methods for building defects detection, adapting an updated PRISMA guideline to ensure a rigorous and transparent review process. The review reveals that convolutional neural networks (CNNs) and their variations are the most popular in this field. By systematically identifying and categorizing the most relevant articles, we present a detailed taxonomy of the methods and applications. Additionally, the article explores current trends and discusses future directions, including advancements in real-time defect detection and the utilization of more diverse and comprehensive datasets.