Nothing Special   »   [go: up one dir, main page]

Zhang et al., 2023 - Google Patents

Identification of concrete surface damage based on probabilistic deep learning of images

Zhang et al., 2023

Document ID
18423571720868328336
Author
Zhang Y
Ni Y
Jia X
Wang Y
Publication year
Publication venue
Automation in Construction

External Links

Snippet

Crack is common damage that can reduce the durability of concrete structures and accelerate structural degradation. With intent to improve the accuracy and efficiency of image-based crack detection, deep learning has been extensively used. However, the …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • 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
    • G06N3/08—Learning methods
    • 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
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • 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
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • 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
    • 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
    • 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
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • 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
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications

Similar Documents

Publication Publication Date Title
Sajedi et al. Uncertainty‐assisted deep vision structural health monitoring
Forkan et al. CorrDetector: A framework for structural corrosion detection from drone images using ensemble deep learning
US11921566B2 (en) Abnormality detection system, abnormality detection method, abnormality detection program, and method for generating learned model
Hou et al. Deep features based on a DCNN model for classifying imbalanced weld flaw types
Sony et al. Vibration-based multiclass damage detection and localization using long short-term memory networks
Miao et al. Pixel‐level multicategory detection of visible seismic damage of reinforced concrete components
Zhang et al. Identification of concrete surface damage based on probabilistic deep learning of images
Yu et al. Automated damage diagnosis of concrete jack arch beam using optimized deep stacked autoencoders and multi-sensor fusion
Zhang et al. Review of artificial intelligence-based bridge damage detection
Maeda et al. Convolutional sparse coding‐based deep random vector functional link network for distress classification of road structures
Li et al. Pixel-level bridge crack detection using a deep fusion about recurrent residual convolution and context encoder network
Wang et al. Bayesian modeling of external corrosion in underground pipelines based on the integration of Markov chain Monte Carlo techniques and clustered inspection data
Sajedi et al. Dual Bayesian inference for risk‐informed vibration‐based damage diagnosis
Savino et al. Automated classification of civil structure defects based on convolutional neural network
Wang et al. Knowledge transfer for structural damage detection through re-weighted adversarial domain adaptation
Cadini et al. Particle filtering‐based adaptive training of neural networks for real‐time structural damage diagnosis and prognosis
Tang et al. Autoregressive model-based structural damage identification and localization using convolutional neural networks
Abubakr et al. Application of deep learning in damage classification of reinforced concrete bridges
Fritz et al. Fault diagnosis in structural health monitoring systems using signal processing and machine learning techniques
Zhang et al. MobileNetV3-BLS: A broad learning approach for automatic concrete surface crack detection
Das et al. Application of deep convolutional neural networks for automated and rapid identification and computation of crack statistics of thin cracks in strain hardening cementitious composites (SHCCs)
He et al. A systematic method of remaining useful life estimation based on physics-informed graph neural networks with multisensor data
CN115545334B (en) Land utilization type prediction method and device, electronic equipment and storage medium
He et al. Uncertainty quantification in multiaxial fatigue life prediction using Bayesian neural networks
Yu et al. Physics-guided generative adversarial network for probabilistic structural system identification