Zhou et al., 2021 - Google Patents
Convolutional neural networks–based model for automated sewer defects detection and classificationZhou et al., 2021
View PDF- Document ID
- 10567289200538050140
- Author
- Zhou Q
- Situ Z
- Teng S
- Chen G
- Publication year
- Publication venue
- Journal of Water Resources Planning and Management
External Links
Snippet
Automated detection and classification of sewer defects can complement the conventional labor-intensive sewer inspection process by providing an essential tool to classify sewer defects in a more efficient, accurate, and consistent way. This paper presents a …
- 238000001514 detection method 0 title abstract description 48
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
- G06K9/36—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
- G06K9/4604—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
-
- 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
- G06T2207/30004—Biomedical image processing
-
- 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
- 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
- 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
-
- 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
- G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
-
- 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
- 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
- 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
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
-
- 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
- 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 |
|---|---|---|
| Zhou et al. | Convolutional neural networks–based model for automated sewer defects detection and classification | |
| Kumar et al. | Deep learning–based automated detection of sewer defects in CCTV videos | |
| Zhang et al. | An efficient lightweight convolutional neural network for industrial surface defect detection | |
| Wang et al. | Automatic concrete crack segmentation model based on transformer | |
| Chun et al. | Automatic detection method of cracks from concrete surface imagery using two‐step light gradient boosting machine | |
| Joshi et al. | Automatic surface crack detection using segmentation-based deep-learning approach | |
| Zhang et al. | Self-supervised structure learning for crack detection based on cycle-consistent generative adversarial networks | |
| Deng et al. | Vision based pixel-level bridge structural damage detection using a link ASPP network | |
| Xue et al. | A fast detection method via region‐based fully convolutional neural networks for shield tunnel lining defects | |
| Shang et al. | Automatic Pixel-level pavement sealed crack detection using Multi-fusion U-Net network | |
| Dong et al. | Innovative method for pavement multiple damages segmentation and measurement by the Road-Seg-CapsNet of feature fusion | |
| Li et al. | A grid‐based classification and box‐based detection fusion model for asphalt pavement crack | |
| Albatayneh et al. | Image retraining using TensorFlow implementation of the pretrained inception-v3 model for evaluating gravel road dust | |
| Mohammed Abdelkader et al. | Entropy-based automated method for detection and assessment of spalling severities in reinforced concrete bridges | |
| Miao et al. | Deep learning‐based evaluation for mechanical property degradation of seismically damaged RC columns | |
| Zhou et al. | Comparative effectiveness of data augmentation using traditional approaches versus stylegans in automated sewer defect detection | |
| Shehab et al. | Automated detection and classification of infiltration in sewer pipes | |
| Li et al. | A novel model for the pavement distress segmentation based on multi-level attention DeepLabV3+ | |
| Zhou et al. | Comparison of classic object-detection techniques for automated sewer defect detection | |
| Yang et al. | Classification and localization of maize leaf spot disease based on weakly supervised learning | |
| Ni et al. | Toward high-precision crack detection in concrete bridges using deep learning | |
| Xue et al. | Adaptive cross-scenario few-shot learning framework for structural damage detection in civil infrastructure | |
| Kumar et al. | Feasibility analysis of convolution neural network models for classification of concrete cracks in Smart City structures | |
| Ni et al. | An improved deep network-based RGB-D semantic segmentation method for indoor scenes | |
| Patel et al. | Semantic segmentation of cracks on masonry surfaces using deep-learning techniques |