Qi et al., 2020 - Google Patents
A CNN-based method for concreate crack detection in underwater environmentsQi et al., 2020
- Document ID
- 3254902399347434490
- Author
- Qi Z
- Zhang J
- Liu D
- Publication year
- Publication venue
- Construction Research Congress 2020
External Links
Snippet
Pre-stressed concrete cylinder pipe (PCCP) is used in pipelines for long-distance water conveyance. However, due to complicated underwater environments, many vision-based concrete crack detection methods cannot be directly applied to the internal surface of the …
- 238000001514 detection method 0 title abstract description 30
Classifications
-
- 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
- G06T2207/20092—Interactive image processing based on input by user
- G06T2207/20104—Interactive definition of region of interest [ROI]
-
- 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
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Rezaie et al. | Comparison of crack segmentation using digital image correlation measurements and deep learning | |
KR102008973B1 (en) | Apparatus and Method for Detection defect of sewer pipe based on Deep Learning | |
Liu et al. | Computer vision-based concrete crack detection using U-net fully convolutional networks | |
Pan et al. | Automatic sewer pipe defect semantic segmentation based on improved U-Net | |
CN112258496A (en) | Underground drainage pipeline disease segmentation method based on full convolution neural network | |
CN113075065B (en) | Deep sea pipeline crack propagation monitoring and reliability evaluation system based on image recognition | |
Shahrokhinasab et al. | Performance of image-based crack detection systems in concrete structures | |
Ye et al. | Diagnosis of sewer pipe defects on image recognition of multi-features and support vector machine in a southern Chinese city | |
Qi et al. | A CNN-based method for concreate crack detection in underwater environments | |
CN114596266B (en) | Concrete crack detection method based on ConcreteCrackSegNet model | |
CN117173461A (en) | Multi-visual task filling container defect detection method, system and medium | |
CN112686217A (en) | Mask R-CNN-based detection method for disease pixel level of underground drainage pipeline | |
Oyedeji et al. | Application of CNN for multiple phase corrosion identification and region detection | |
Ebrahimi et al. | Probabilistic condition assessment of reinforced concrete sanitary sewer pipelines using LiDAR inspection data | |
Jung et al. | 3d imaging and analysis of cracks in loaded concrete samples | |
Khalifa et al. | A new image-based model for predicting cracks in sewer pipes | |
Chen et al. | Deep learning based underground sewer defect classification using a modified RegNet | |
CN118334007A (en) | Crack detection and early warning method and system for hydraulic concrete structure | |
CN115063337A (en) | Intelligent maintenance decision-making method and device for buried pipeline | |
CN117132575A (en) | Improved PointNet++ based three-dimensional detection method for defects of drainage pipeline | |
CN113781513B (en) | Leakage detection method and system for water supply pipeline of power plant | |
CN113065224A (en) | Deep sea pipeline crack propagation monitoring and reliability evaluation method based on image recognition | |
Sinha et al. | Corrosion estimation of underwater structures employing bag of features (BoF) | |
Fu et al. | Detection and recognition of metal surface corrosion based on CBG-YOLOv5s | |
Gao et al. | Leak Detection of Underwater Pipelines Based on Machine Learning Algorithms |