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Intelligent Crack Detection and Quantification in the Concrete Bridge: A Deep Learning‐Assisted Image Processing Approach

https://doi.org/10.1155/2022/1813821

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Localization Recall Precision (LRP): A New Performance Metric for Object Detection

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Learning Data Augmentation Strategies for Object Detection

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SSD: Single Shot MultiBox Detector

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A novel method for asphalt pavement crack classification based on image processing and machine learning

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Optimizing two-pass connected-component labeling algorithms

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The Pascal Visual Object Classes Challenge: A Retrospective

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Contrast Limited Adaptive Histogram Equalization

inferredcites →https://doi.org/10.1016/j.autcon.2013.10.021openalex-snapshot

Long-distance precision inspection method for bridge cracks with image processing

inferredcites →https://doi.org/10.1016/j.cemconres.2017.04.018openalex-snapshot

Comparative analysis of image binarization methods for crack identification in concrete structures

inferredcites →https://doi.org/10.1016/j.ijleo.2015.09.147openalex-snapshot

Detection crack in image using Otsu method and multiple filtering in image processing techniques

inferredcites →https://doi.org/10.1016/j.jtte.2019.10.001openalex-snapshot

Pavement crack image acquisition methods and crack extraction algorithms: A review

inferredcites →https://doi.org/10.1023/a:1017181826899openalex-snapshot

Glossary of Terms

inferredcites →https://doi.org/10.1061/(asce)0887-3801(2003)17:4(255)openalex-snapshot

Analysis of Edge-Detection Techniques for Crack Identification in Bridges

inferredcites →https://doi.org/10.1088/1361-6501/ab79c8openalex-snapshot

Automatic crack recognition for concrete bridges using a fully convolutional neural network and naive Bayes data fusion based on a visual detection system

inferredcites →https://doi.org/10.1109/access.2019.2939201openalex-snapshot

A Survey of Deep Learning-Based Object Detection

inferredcites →https://doi.org/10.1109/cvpr.2015.7298965openalex-snapshot

Fully convolutional networks for semantic segmentation

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Deep Residual Learning for Image Recognition

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You Only Look Once: Unified, Real-Time Object Detection

inferredcites →https://doi.org/10.1109/cvpr.2017.106openalex-snapshot

Feature Pyramid Networks for Object Detection

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Aggregated Residual Transformations for Deep Neural Networks

inferredcites →https://doi.org/10.1109/cvpr.2017.690openalex-snapshot

YOLO9000: Better, Faster, Stronger

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Path Aggregation Network for Instance Segmentation

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Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression

inferredcites →https://doi.org/10.1109/cvpr42600.2020.01079openalex-snapshot

EfficientDet: Scalable and Efficient Object Detection

inferredcites →https://doi.org/10.1109/cvprw50498.2020.00203openalex-snapshot

CSPNet: A New Backbone that can Enhance Learning Capability of CNN

inferredcites →https://doi.org/10.1109/iccv.2015.169openalex-snapshot

Fast R-CNN

inferredcites →https://doi.org/10.1109/iccv.2017.322openalex-snapshot

Mask R-CNN

inferredcites →https://doi.org/10.1109/icemi.2017.8265710openalex-snapshot

Canny edge detection based on Open CV

inferredcites →https://doi.org/10.1109/iciap.1999.797615openalex-snapshot

A simple and efficient connected components labeling algorithm

inferredcites →https://doi.org/10.1109/iscid.2019.10086openalex-snapshot

Crack Detection of Track Plate Based on YOLO

inferredcites →https://doi.org/10.1111/mice.12263openalex-snapshot

Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks

inferredcites →https://doi.org/10.1111/mice.12334openalex-snapshot

Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage Types

inferredcites →https://doi.org/10.1111/mice.12412openalex-snapshot

Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network

inferredcites →https://doi.org/10.1111/mice.12500openalex-snapshot

Concrete bridge surface damage detection using a single-stage detector

inferredcites →https://doi.org/10.1155/2021/3137083openalex-snapshot

Concrete Crack Detection Algorithm Based on Deep Residual Neural Networks

inferredcites →https://doi.org/10.1155/2021/5573590openalex-snapshot

Pavement Crack Detection Method Based on Deep Learning Models

inferredcites →https://doi.org/10.1155/2021/9934250openalex-snapshot

Crack Identification Method of Steel Fiber Reinforced Concrete Based on Deep Learning: A Comparative Study and Shared Crack Database

inferredcites →https://doi.org/10.3390/s20030717openalex-snapshot

Automatic Tunnel Crack Detection Based on U-Net and a Convolutional Neural Network with Alternately Updated Clique

inferredcites →https://doi.org/10.4028/www.scientific.net/amr.487.622openalex-snapshot

A Crack Segmentation Approach Using the Combination of Gray Thresholds and Fractal Feature

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Multimodal Deep Learning-Based Method for Automatic Generation of Repair Method for Steel Bridge Shallow Damage

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Holistic image‐based analysis of damage on concrete surfaces—A multifaceted approach based on supervised machine learning

inferred← citeshttps://doi.org/10.1007/978-981-97-8975-7_12openalex-snapshot

Application of Image Processing Techniques for Analyzing Strength of Concrete Structures—A Novel Approach

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An improved Yolov8-MED model for automated detection of pavement cracks

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Vision based defect detection technologies in civil structures: a review study

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Automated multi-type damage detection framework in reinforced concrete structures via data augmentation and deep segmentation networks

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Improved Parallel Convolutional Neural Network with Proposed Semantic Segmentation for Concrete Surface Crack Segmentation and Classification

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Thermal imagery temperature gradient analysis through deformable transformer residual regression network for crack severity estimation

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Surface defect detection of civil structures using images: Review from data perspective

inferred← citeshttps://doi.org/10.1016/j.autcon.2024.105331openalex-snapshot

Pavement crack instance segmentation using YOLOv7-WMF with connected feature fusion

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Multimodal deep learning-based automatic generation of repair proposals for steel bridge shallow damage

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Flexible and stone pavements distress detection and measurement by deep learning and low-cost detection devices

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Efficient hybrid ensembles of CNNs and transfer learning models for bridge deck image-based crack detection

inferred← citeshttps://doi.org/10.1016/j.istruc.2025.108800openalex-snapshot

A review of crack research in concrete structures based on data-driven and intelligent algorithms

inferred← citeshttps://doi.org/10.1016/j.jobe.2024.110650openalex-snapshot

A non-local based microcrack segmentation model optimized for effective high resolution and low-power devices

inferred← citeshttps://doi.org/10.1016/j.procs.2026.07.310openalex-snapshot

Intelligent Bridge Defect Detection Algorithm Integrating Transformer and Machine Vision and Its Engineering Application

inferred← citeshttps://doi.org/10.1061/jpcfev.cfeng-4671openalex-snapshot

Multidamage Identification in High-Resolution Concrete Bridge Component Imagery Based on Deep Learning

inferred← citeshttps://doi.org/10.1080/10589759.2025.2474708openalex-snapshot

Research on concrete dam crack identification and localisation based on CDI-AR convolutional neural network

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AI-Powered Crack Detection System for Structural Monitoring

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Image-Based Crack Detection System: A Review

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Crevices Recognition on Asphalt Surfaces using Convolutional Neural Network

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Estimation of Flexural Crack Width in Reinforced Concrete Beams Using International Codes: A Parametric Analysis

inferred← citeshttps://doi.org/10.1155/stc/2011149openalex-snapshot

Crack Detection and Localization for Concrete Structures Using UAV Incorporating Dual‐Camera Systems With Heterogeneous Lenses

inferred← citeshttps://doi.org/10.1177/13694332251381215openalex-snapshot

Enhancing structural health monitoring: Conditional GAN-based crack detection in concrete and asphalt surfaces

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Efficient surface crack segmentation for industrial and civil applications based on an enhanced YOLOv8 model

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Civil Structural Health Monitoring and Machine Learning: A Comprehensive Review

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Inspection Robot and Wall Surface Detection Method for Coal Mine Wind Shaft

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Synthesized Evaluation of Reinforced Concrete Bridge Defects, Their Non-Destructive Inspection and Analysis Methods: A Systematic Review and Bibliometric Analysis of the Past Three Decades

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Spiking VGG7: Deep Convolutional Spiking Neural Network with Direct Training for Object Recognition

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Towards Robotic Marble Resin Application: Crack Detection on Marble Using Deep Learning

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Tunnel Lining Crack Recognition Algorithm Integrating SK Attention and Cascade Neural Network

inferred← citeshttps://doi.org/10.3390/electronics13224426openalex-snapshot

Mixed Reality-Based Concrete Crack Detection and Skeleton Extraction Using Deep Learning and Image Processing

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CNN-Based Automatic Mobile Reporting System and Quantification for the Concrete Crack Size of the Precast Members of OSC Construction

inferred← citeshttps://doi.org/10.3390/math12193105openalex-snapshot

Co-CrackSegment: A New Collaborative Deep Learning Framework for Pixel-Level Semantic Segmentation of Concrete Cracks

inferred← citeshttps://doi.org/10.3390/s23031419openalex-snapshot

Fast Detection of Missing Thin Propagating Cracks during Deep-Learning-Based Concrete Crack/Non-Crack Classification

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Research on the Efficiency of Bridge Crack Detection by Coupling Deep Learning Frameworks with Convolutional Neural Networks

inferred← citeshttps://doi.org/10.3390/s24134288openalex-snapshot

A Novel Model for Instance Segmentation and Quantification of Bridge Surface Cracks—The YOLOv8-AFPN-MPD-IoU

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A Calibrated Post-Processing Layer for Tolerance-Compliant Crack-Width Measurement

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Wall Crack Detection based on Adaptive Double Threshold Greyscale Transform

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