Explainable AI for detecting and monitoring infrastructure defects

AI can help improve railway safety by enabling automated inspections of tracks, crossties, ballasts and retaining walls. Researchers at EPFL's Intelligent Maintenance and Operations Systems (IMOS) Laboratory have developed an AI-driven method that improves the efficiency of crack detection in concrete structures. Their research, recently published in Automation in Construction, introduces a novel method that employs explainable artificial intelligence, or a form of AI which allows users to understand the basis of AI decisions. "We trained an algorithm to differentiate between images with and without cracks in concrete walls [a binary classification task] by feeding it hundreds of image samples from both categories. Then we asked the algorithm to highlight which pixels it used to make its decision," says Florent Forest, a scientist at the IMOS lab and the study's lead author.