[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Qi et al., 2020 - Google Patents

A CNN-based method for concreate crack detection in underwater environments

Qi 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 …
Continue reading at ascelibrary.org (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30108Industrial image inspection
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20092Interactive image processing based on input by user
    • G06T2207/20104Interactive definition of region of interest [ROI]
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20112Image segmentation details
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/84Systems specially adapted for particular applications
    • G01N21/88Investigating the presence of flaws or contamination
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10024Color image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/0002Inspection 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