Fan, 2021 - Google Patents
Evaluation of Machine Learning Methods for Image Classification: A Case Study of Facility Surface DamageFan, 2021
- Document ID
- 1811528588863984453
- Author
- Fan C
- Publication year
- Publication venue
- International Conference on Machine Learning for Networking
External Links
Snippet
Common reinforced concrete (RC) damage includes exposed rebars, spalling, and efflorescence, which not only affect the aesthetics of facilities but also cause structural degradation over time, setting the stage for further severe RC degradation that would reduce …
Classifications
-
- 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
- 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
- G06T2207/30164—Workpiece; Machine component
-
- 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
- G06K9/6228—Selecting the most significant subset of features
-
- 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
- G06K9/6279—Classification techniques relating to the number of classes
-
- 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
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
-
- 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
- G06F17/30244—Information retrieval; Database structures therefor; File system structures therefor in image databases
-
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/40—Analysis of texture
-
- 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
- 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
- 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
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Meijer et al. | A defect classification methodology for sewer image sets with convolutional neural networks | |
Jahanshahi et al. | An innovative methodology for detection and quantification of cracks through incorporation of depth perception | |
US9361702B2 (en) | Image detection method and device | |
Ahmadi et al. | An integrated machine learning model for automatic road crack detection and classification in urban areas | |
Myrans et al. | Automated detection of fault types in CCTV sewer surveys | |
Zhao et al. | A sparse-representation-based robust inspection system for hidden defects classification in casting components | |
Kim et al. | Automated classification of thermal defects in the building envelope using thermal and visible images | |
Jiang et al. | A robust bridge rivet identification method using deep learning and computer vision | |
Park et al. | Learning‐based image scale estimation using surface textures for quantitative visual inspection of regions‐of‐interest | |
Devereux et al. | A new approach for crack detection and sizing in nuclear reactor cores | |
Kirthiga et al. | A survey on crack detection in concrete surface using image processing and machine learning | |
Apeagyei et al. | Evaluation of deep learning models for classification of asphalt pavement distresses | |
Gonthina et al. | Deep CNN-based concrete cracks identification and quantification using image processing techniques | |
Rashid et al. | Low-resolution image classification of cracked concrete surface using decision tree technique | |
Zhang et al. | Concrete crack quantification using voxel-based reconstruction and Bayesian data fusion | |
Mazni et al. | An investigation into real-time surface crack classification and measurement for structural health monitoring using transfer learning convolutional neural networks and Otsu method | |
Guo et al. | Visual pattern recognition supporting defect reporting and condition assessment of wastewater collection systems | |
Nooralishahi et al. | PHM-IRNET: Self-training thermal segmentation approach for thermographic inspection of industrial components | |
Pratibha et al. | Deep learning-based yolo network model for detecting surface cracks during structural health monitoring | |
Fan | Evaluation of Machine Learning Methods for Image Classification: A Case Study of Facility Surface Damage | |
Guldur et al. | Automated classification of detected surface damage from point clouds with supervised learning | |
Kasireddy et al. | Encoding 3d point contexts for self-supervised spall classification using 3d bridge point clouds | |
Chen et al. | Automated detection of sewer pipe defects based on cost-sensitive convolutional neural network | |
Danajitha et al. | Detection of cracks in high rise buildings using drones | |
Pozzer et al. | Enhancing concrete defect segmentation using multimodal data and Siamese Neural Networks |