Li et al., 2021 - Google Patents
Sewer pipe defect detection via deep learning with local and global feature fusionLi et al., 2021
- Document ID
- 4534598911193743933
- Author
- Li D
- Xie Q
- Yu Z
- Wu Q
- Zhou J
- Wang J
- Publication year
- Publication venue
- Automation in Construction
External Links
Snippet
The damages triggered by the long-term corrosion, external disturbance, and uneven support pressure result in defective states of sewer pipes. Nowadays, the sewer pipe images are easily captured by closed-circuit televisions (CCTV) and quick-view (QV). However, the …
- 238000001514 detection method 0 title abstract description 100
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
- G06K9/6217—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
- 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
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- 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
- G06K9/00624—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
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
-
- 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
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Li et al. | Sewer pipe defect detection via deep learning with local and global feature fusion | |
Li et al. | Automatic defect detection of metro tunnel surfaces using a vision-based inspection system | |
Kou et al. | Development of a YOLO-V3-based model for detecting defects on steel strip surface | |
Ali et al. | Structural crack detection using deep convolutional neural networks | |
Xue et al. | A fast detection method via region‐based fully convolutional neural networks for shield tunnel lining defects | |
Jha et al. | Deep CNN-based visual defect detection: Survey of current literature | |
CN108876780B (en) | Bridge crack image crack detection method under complex background | |
Wan et al. | LFRNet: Localizing, focus, and refinement network for salient object detection of surface defects | |
Fan et al. | Pavement defect detection with deep learning: A comprehensive survey | |
Zhou et al. | EDDs: A series of Efficient Defect Detectors for fabric quality inspection | |
Zheng et al. | CASPPNet: A chained atrous spatial pyramid pooling network for steel defect detection | |
Liu et al. | Defect detection of the surface of wind turbine blades combining attention mechanism | |
Gou et al. | Pavement crack detection based on the improved faster-rcnn | |
Jia et al. | Intelligent identification of metal corrosion based on Corrosion-YOLOv5s | |
Qiu et al. | A lightweight yolov4-edam model for accurate and real-time detection of foreign objects suspended on power lines | |
Wang et al. | Automatic identification and location of tunnel lining cracks | |
Ye et al. | Pavement crack instance segmentation using YOLOv7-WMF with connected feature fusion | |
Lu et al. | MSCNet: a framework with a texture enhancement mechanism and feature aggregation for crack detection | |
Zhang et al. | Automated detection and segmentation of tunnel defects and objects using YOLOv8-CM | |
Cheng et al. | EC-YOLO: Effectual Detection Model for Steel Strip Surface Defects Based on YOLO-V5 | |
Kumar et al. | Feasibility analysis of convolution neural network models for classification of concrete cracks in Smart City structures | |
Wang et al. | Automatic detection of building surface cracks using UAV and deep learning‐combined approach | |
Ashraf et al. | Efficient Pavement Crack Detection and Classification Using Custom YOLOv7 Model | |
Ibragimov et al. | Automated Pavement Condition Index Assessment with Deep Learning and Image Analysis: An End-to-End Approach | |
Li et al. | Wooden spoon crack detection by prior knowledge-enriched deep convolutional network |