Fan et al., 2022 - Google Patents
Urban digital twins for intelligent road inspectionFan et al., 2022
View PDF- Document ID
- 15541445246712889024
- Author
- Fan R
- Zhang Y
- Guo S
- Li J
- Feng Y
- Su S
- Zhang Y
- Wang W
- Jiang Y
- Bocus M
- Zhu X
- Chen Q
- Publication year
- Publication venue
- 2022 IEEE International Conference on Big Data (Big Data)
External Links
Snippet
Urban digital twin (UDT) technologies offer new opportunities for intelligent road inspection (IRI). This paper first reviews the state-of-the-art algorithms used in the two key components of UDT-based IRI systems:(1) multi-temporal, multi-dimension, multi-score, and …
- 238000007689 inspection 0 title abstract description 9
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/05—Geographic models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Alipour et al. | Robust pixel-level crack detection using deep fully convolutional neural networks | |
Yu et al. | A real-time detection approach for bridge cracks based on YOLOv4-FPM | |
Jiang et al. | A deep learning approach for fast detection and classification of concrete damage | |
CN111476781B (en) | Concrete crack identification method and device based on video semantic segmentation technology | |
Luo et al. | Autonomous detection of damage to multiple steel surfaces from 360 panoramas using deep neural networks | |
Sofia et al. | Mobile mapping, machine learning and digital twin for road infrastructure monitoring and maintenance: Case study of mohammed VI bridge in Morocco | |
Huang et al. | Underwater dam crack image generation based on unsupervised image-to-image translation | |
Çelik et al. | A sigmoid‐optimized encoder–decoder network for crack segmentation with copy‐edit‐paste transfer learning | |
Jing et al. | Segmentation of large-scale masonry arch bridge point clouds with a synthetic simulator and the BridgeNet neural network | |
König et al. | What's cracking? A review and analysis of deep learning methods for structural crack segmentation, detection and quantification | |
Huang et al. | Image-based automatic multiple-damage detection of concrete dams using region-based convolutional neural networks | |
Smith et al. | Advanced Computing Strategies for Engineering: 25th EG-ICE International Workshop 2018, Lausanne, Switzerland, June 10-13, 2018, Proceedings, Part I | |
Mondal et al. | Artificial intelligence in civil infrastructure health monitoring—Historical perspectives, current trends, and future visions | |
Chen et al. | Improving completeness and accuracy of 3D point clouds by using deep learning for applications of digital twins to civil structures | |
Jiang et al. | Computer Vision Applications In Construction And Asset Management Phases: A Literature Review. | |
Fan et al. | Urban digital twins for intelligent road inspection | |
Fan et al. | Computer-aided road inspection: Systems and algorithms | |
Wei et al. | Panorama-to-model registration through integration of image retrieval and semantic reprojection | |
Zhao et al. | High-resolution infrastructure defect detection dataset sourced by unmanned systems and validated with deep learning | |
Chen et al. | Shifting research from defect detection to defect modeling in computer vision-based structural health monitoring | |
Ma et al. | Attention‐optimized 3D segmentation and reconstruction system for sewer pipelines employing multi‐view images | |
Dong et al. | CBAM-optimized automatic segmentation and reconstruction system for monocular images with asphalt pavement potholes | |
De Geyter et al. | Automated training data creation for semantic segmentation of 3D point clouds | |
Li et al. | [Retracted] PointLAE: A Point Cloud Semantic Segmentation Neural Network via Multifeature Aggregation for Large‐Scale Application | |
Jing et al. | Anomaly detection of cracks in synthetic masonry arch bridge point clouds using fast point feature histograms and PatchCore |