Ashraf et al., 2023 - Google Patents

Machine learning-based pavement crack detection, classification, and characterization: a review

Ashraf et al., 2023

View PDF
Document ID
2218712238717541142
Author
Ashraf A
Sophian A
Shafie A
Gunawan T
Ismail N
Publication year
Publication venue
Bulletin of Electrical Engineering and Informatics

External Links

Snippet

The detection, classification, and characterization of pavement cracks are critical for maintaining safe road conditions. However, traditional manual inspection methods are slow, costly, and pose risks to inspectors. To address these issues, this article provides a …
Continue reading at beei.org (PDF) (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
    • G06T2207/30Subject 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
    • G06T2207/20112Image segmentation details
    • 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
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • 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
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30004Biomedical image processing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection
    • G06T7/0004Industrial image inspection
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • 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
    • 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
    • G06K9/20Image acquisition
    • 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/002D [Two Dimensional] image generation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q10/00Administration; Management
    • 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
Hsieh et al. Machine learning for crack detection: Review and model performance comparison
Ji et al. An integrated approach to automatic pixel-level crack detection and quantification of asphalt pavement
Majidifard et al. Pavement image datasets: A new benchmark dataset to classify and densify pavement distresses
Kalfarisi et al. Crack detection and segmentation using deep learning with 3D reality mesh model for quantitative assessment and integrated visualization
Tan et al. Automatic detection of sewer defects based on improved you only look once algorithm
Safaei et al. An automatic image processing algorithm based on crack pixel density for pavement crack detection and classification
Tran et al. A two-step sequential automated crack detection and severity classification process for asphalt pavements
Chen et al. A self organizing map optimization based image recognition and processing model for bridge crack inspection
Zhou et al. Deep learning-based crack segmentation for civil infrastructure: Data types, architectures, and benchmarked performance
Miao et al. Cost-effective system for detection and quantification of concrete surface cracks by combination of convolutional neural network and image processing techniques
Gupta et al. Image-based crack detection approaches: a comprehensive survey
Ashraf et al. Machine learning-based pavement crack detection, classification, and characterization: a review
Wu et al. Applying deep convolutional neural network with 3D reality mesh model for water tank crack detection and evaluation
CN111008956B (en) Beam bottom crack detection method, system, device and medium based on image processing
Matarneh et al. Evaluation and optimisation of pre-trained CNN models for asphalt pavement crack detection and classification
Lang et al. Pavement cracking detection and classification based on 3d image using multiscale clustering model
Mokhtari et al. Statistical selection and interpretation of imagery features for computer vision-based pavement crack–detection systems
Wang et al. Crack image recognition on fracture mechanics cross valley edge detection by fractional differential with multi-scale analysis
Bahreini et al. Dynamic graph CNN based semantic segmentation of concrete defects and as-inspected modeling
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
Ashraf et al. Efficient Pavement Crack Detection and Classification Using Custom YOLOv7 Model
Kulambayev et al. Real-time road surface damage detection framework based on mask r-cnn model
Ji et al. A transformer-based deep learning method for automatic pixel-level crack detection and feature quantification
Li et al. Automated bridge crack detection based on improving encoder–decoder network and strip pooling