Ye et al., 2024 - Google Patents
Pavement crack instance segmentation using YOLOv7-WMF with connected feature fusionYe et al., 2024
- Document ID
- 2976822439725691200
- Author
- Ye G
- Li S
- Zhou M
- Mao Y
- Qu J
- Shi T
- Jin Q
- Publication year
- Publication venue
- Automation in Construction
External Links
Snippet
The detection and classification of concrete damage is essential for maintaining good infrastructure condition. Traditional semantic segmentation methods often can not provide accurate crack boundary information, which limits the further location and measurement …
- 230000011218 segmentation 0 title abstract description 71
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
- G06K9/4609—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections by matching or filtering
-
- 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
- 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
- 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
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- 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
-
- 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
- G06T2207/10—Image acquisition modality
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T1/00—General purpose image data processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2200/00—Indexing scheme for image data processing or generation, in general
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K2209/00—Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
-
- 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
- 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
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ye et al. | Pavement crack instance segmentation using YOLOv7-WMF with connected feature fusion | |
| Huyan et al. | Detection of sealed and unsealed cracks with complex backgrounds using deep convolutional neural network | |
| Wang et al. | Automatic concrete crack segmentation model based on transformer | |
| Ghosh Mondal et al. | Deep learning‐based multi‐class damage detection for autonomous post‐disaster reconnaissance | |
| Alipour et al. | Robust pixel-level crack detection using deep fully convolutional neural networks | |
| Ye et al. | Autonomous surface crack identification of concrete structures based on the YOLOv7 algorithm | |
| Xue et al. | A fast detection method via region‐based fully convolutional neural networks for shield tunnel lining defects | |
| Xiao et al. | Region of interest (ROI) extraction and crack detection for UAV-based bridge inspection using point cloud segmentation and 3D-to-2D projection | |
| Li et al. | A deep learning-based fine crack segmentation network on full-scale steel bridge images with complicated backgrounds | |
| Sarhadi et al. | Optimizing concrete crack detection: An attention-based swin u-net approach | |
| Deng et al. | Cascade refinement extraction network with active boundary loss for segmentation of concrete cracks from high-resolution images | |
| Chu et al. | A transformer and self-cascade operation-based architecture for segmenting high-resolution bridge cracks | |
| Dong et al. | Intelligent segmentation and measurement model for asphalt road cracks based on modified mask R-CNN algorithm | |
| Fu et al. | Detecting surface defects of heritage buildings based on deep learning | |
| Munawar et al. | Modern crack detection for bridge infrastructure maintenance using machine learning | |
| Sun et al. | DUCTNet: an effective road crack segmentation method in UAV remote sensing images under complex scenes | |
| Ji et al. | A transformer-based deep learning method for automatic pixel-level crack detection and feature quantification | |
| Zheng et al. | Railway side slope hazard detection system based on generative models | |
| Kumar et al. | Feasibility analysis of convolution neural network models for classification of concrete cracks in Smart City structures | |
| Ye et al. | Intelligent Detection of Surface Defects in High‐Speed Railway Ballastless Track Based on Self‐Attention and Transfer Learning | |
| Li et al. | Automatic concrete crack identification based on lightweight embedded U-Net | |
| Zhou et al. | MixSegNet: a novel crack segmentation network combining CNN and Transformer | |
| Wang et al. | An optimized and precise road crack segmentation network in complex scenarios | |
| Luo et al. | A Road Damage Detection Model Based on Improved RT-DETR for Complex Environments | |
| Gao et al. | EU-Net: a segmentation network based on semantic fusion and edge guidance for road crack images |