Real-time Quantitative Visual Inspection using Extended Reality


 In this study, we propose a technique for quantitative visual inspection that can quantify structural damage using extended reality (XR). The XR headset display and overlay graphical information on the physical space process data from built-in camera depth sensor. Also, device permits accessing analyzing image video stream in real-time utilizing 3D meshes of environment pose information. By leveraging these features headset, build workflow graphic interface to capture images, segment regions, evaluate size damage. A deep learning-based interactive segmentation algorithm called f-BRS was deployed precisely regions through headset. ray-casting is implemented obtain locations corresponding pixel region image. computed its boundary. performance proposed method demonstrated field experiment at an in-service bridge where spalling present abutment. shows provides sub-centimeter accuracy estimation.