Nothing Special   »   [go: up one dir, main page]

Tran et al., 2021 - Google Patents

One stage detector (RetinaNet)-based crack detection for asphalt pavements considering pavement distresses and surface objects

Tran et al., 2021

Document ID
7003899670016024
Author
Tran V
Tran T
Lee H
Kim K
Baek J
Nguyen T
Publication year
Publication venue
Journal of Civil Structural Health Monitoring

External Links

Snippet

In this study, a supervised machine learning network model is proposed to detect and classify various types of cracks developed in asphalt pavements, including lane markers. Crack images captured from a digital camera are classified into nine categories following the …
Continue reading at link.springer.com (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • 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
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10032Satellite or aerial image; Remote sensing
    • 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
    • G06T2207/30181Earth observation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/0063Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
    • 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
    • G06T2207/30004Biomedical image processing
    • 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
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00127Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G06T17/05Geographic models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K2209/00Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints

Similar Documents

Publication Publication Date Title
Tran et al. One stage detector (RetinaNet)-based crack detection for asphalt pavements considering pavement distresses and surface objects
Wu et al. Road pothole extraction and safety evaluation by integration of point cloud and images derived from mobile mapping sensors
Kalfarisi et al. Crack detection and segmentation using deep learning with 3D reality mesh model for quantitative assessment and integrated visualization
Safaei et al. An automatic image processing algorithm based on crack pixel density for pavement crack detection and classification
Tong et al. Innovative method for recognizing subgrade defects based on a convolutional neural network
Rani et al. Road Identification Through Efficient Edge Segmentation Based on Morphological Operations.
Valikhani et al. Machine learning and image processing approaches for estimating concrete surface roughness using basic cameras
Wu et al. Improvement of crack-detection accuracy using a novel crack defragmentation technique in image-based road assessment
Li et al. Automatic pavement-crack detection and segmentation based on steerable matched filtering and an active contour model
Hüthwohl et al. Detecting healthy concrete surfaces
Huyan et al. Pixelwise asphalt concrete pavement crack detection via deep learning‐based semantic segmentation method
Li et al. Automated classification and detection of multiple pavement distress images based on deep learning
Hoang et al. Fast local Laplacian‐based steerable and Sobel filters integrated with adaptive boosting classification tree for automatic recognition of asphalt pavement cracks
Guerrieri et al. Flexible and stone pavements distress detection and measurement by deep learning and low-cost detection devices
Sathya et al. A framework for designing unsupervised pothole detection by integrating feature extraction using deep recurrent neural network
Kuchi et al. A machine learning approach to detecting cracks in levees and floodwalls
Hassan et al. Detecting patches on road pavement images acquired with 3D laser sensors using object detection and deep learning
Mokhtari et al. Statistical selection and interpretation of imagery features for computer vision-based pavement crack–detection systems
Syed et al. Pothole detection under diverse conditions using object detection models
Cano-Ortiz et al. An end-to-end computer vision system based on deep learning for pavement distress detection and quantification
Qureshi et al. Deep learning framework for intelligent pavement condition rating: A direct classification approach for regional and local roads
Zhao et al. High-resolution infrastructure defect detection dataset sourced by unmanned systems and validated with deep learning
Kumar et al. Feasibility analysis of convolution neural network models for classification of concrete cracks in Smart City structures
Jiang A crack detection and diagnosis methodology for automated pavement condition evaluation
Shao et al. Crack detection and measurement using PTZ camera–based image processing method on expressways