Han et al., 2023 - Google Patents
Ceiling damage detection and safety assessment in large public buildings using semantic segmentationHan et al., 2023
- Document ID
- 1819589297750149041
- Author
- Han Q
- Yan S
- Wang L
- Kawaguchi K
- Publication year
- Publication venue
- Journal of Building Engineering
External Links
Snippet
To solve the limitations of traditional on-site inspections by professionals, an automatic method using the semantic segmentation network Deeplabv3+ with transfer learning (TL) is proposed for rapid detection and safety assessment of damaged ceilings in large public …
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
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30148—Semiconductor; IC; Wafer
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
-
- 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
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
-
- 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
-
- 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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
-
- 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
- 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
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Tan et al. | Automatic detection of sewer defects based on improved you only look once algorithm | |
Alipour et al. | Robust pixel-level crack detection using deep fully convolutional neural networks | |
Sony et al. | A systematic review of convolutional neural network-based structural condition assessment techniques | |
Kalfarisi et al. | Crack detection and segmentation using deep learning with 3D reality mesh model for quantitative assessment and integrated visualization | |
Spencer Jr et al. | Advances in computer vision-based civil infrastructure inspection and monitoring | |
Xue et al. | A fast detection method via region‐based fully convolutional neural networks for shield tunnel lining defects | |
Zhu et al. | Visual retrieval of concrete crack properties for automated post-earthquake structural safety evaluation | |
Wu et al. | Enhanced Precision in Dam Crack Width Measurement: Leveraging Advanced Lightweight Network Identification for Pixel‐Level Accuracy | |
Zou et al. | Multicategory damage detection and safety assessment of post‐earthquake reinforced concrete structures using deep learning | |
Ye et al. | Automatic pixel‐level crack detection with multi‐scale feature fusion for slab tracks | |
German et al. | Rapid entropy-based detection and properties measurement of concrete spalling with machine vision for post-earthquake safety assessments | |
Chen et al. | Automatic concrete defect detection and reconstruction by aligning aerial images onto semantic‐rich building information model | |
Jeong et al. | Literature review and technical survey on bridge inspection using unmanned aerial vehicles | |
Ye et al. | Autonomous surface crack identification of concrete structures based on the YOLOv7 algorithm | |
Quqa et al. | Two-step approach for fatigue crack detection in steel bridges using convolutional neural networks | |
Chen et al. | Bottom-up image detection of water channel slope damages based on superpixel segmentation and support vector machine | |
WO2023287276A1 (en) | Geographic data processing methods and systems for detecting encroachment by objects into a geographic corridor | |
Dong et al. | Innovative method for pavement multiple damages segmentation and measurement by the Road-Seg-CapsNet of feature fusion | |
Han et al. | Ceiling damage detection and safety assessment in large public buildings using semantic segmentation | |
Yuan et al. | Automated pixel-level crack detection and quantification using deep convolutional neural networks for structural condition assessment | |
Chen et al. | A deep region-based pyramid neural network for automatic detection and multi-classification of various surface defects of aluminum alloys | |
Wu et al. | Applying deep convolutional neural network with 3D reality mesh model for water tank crack detection and evaluation | |
Guerrieri et al. | Flexible and stone pavements distress detection and measurement by deep learning and low-cost detection devices | |
Ji et al. | Image‐based road crack risk‐informed assessment using a convolutional neural network and an unmanned aerial vehicle | |
Yamaguchi et al. | Quantitative road crack evaluation by a U‐Net architecture using smartphone images and Lidar data |