Drexel’s method, which researchers published in the journal Automation in Construction, uses a high-resolution stereo-depth camera to record images of a structure before feeding them into a deep-learning program called a convolutional neural network.
These systems can spot the finest of patterns and discrepancies in massive volumes of data, researchers said, with applications previously used in drug development, deepfake detection and facial recognition technology.
Arvin Ebrahimkhanlou, an assistant professor at Drexel’s College of Engineering and a member of the research team, told Construction Dive that the team has been in contact with the National Academy of Building Inspection Engineers throughout the design process.
