{
  "$type": "site.standard.document",
  "description": "In various example embodiments, techniques are provided for crack detection, assessment and visualization that utilize deep learning in combination with a 3D mesh model. Deep learning is applied to a set of 2D images of infrastructure to identify and segment surface cracks. For example, a Faster…",
  "path": "/patents/1313787",
  "publishedAt": "2022-03-24T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "G06T19/006",
    "Bentley Systems, Incorporated"
  ],
  "textContent": "In various example embodiments, techniques are provided for crack detection, assessment and visualization that utilize deep learning in combination with a 3D mesh model. Deep learning is applied to a set of 2D images of infrastructure to identify and segment surface cracks. For example, a Faster region-based convolutional neural network (Faster-RCNN) may identify surface cracks and a structured random forest edge detection (SFRED) technique may segment the identified surface cracks. Alternatively, a Mask region-based convolutional neural network (Mask-RCNN) may identify and segment surface cracks in parallel. Photogrammetry is used to generate a textured three-dimensional (3D) mesh model of the infrastructure from the 2D images. A texture cover of the 3D mesh model is analyzed to determine quantitative measures of identified surface cracks. The 3D mesh model is displayed to provide a visualization of identified surface cracks and facilitate inspection of the infrastructure.",
  "title": "CRACK DETECTION, ASSESSMENT AND VISUALIZATION USING DEEP LEARNING WITH 3D MESH MODEL"
}