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SDDNet: Real-Time Crack Segmentation
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Pixel-level crack delineation in images with convolutional feature fusion
inferredcites →https://doi.org/10.1007/978-3-030-01234-2_49openalex-snapshot
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
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U-Net: Convolutional Networks for Biomedical Image Segmentation
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Optimizing Intersection-Over-Union in Deep Neural Networks for Image Segmentation
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Autonomous concrete crack detection using deep fully convolutional neural network
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Deep learning-based automatic volumetric damage quantification using depth camera
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DeepCrack: A deep hierarchical feature learning architecture for crack segmentation
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Deep Learning–Based Fully Automated Pavement Crack Detection on 3D Asphalt Surfaces with an Improved CrackNet
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Automatic classification of pavement crack using deep convolutional neural network
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ImageNet: A large-scale hierarchical image database
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Fully convolutional networks for semantic segmentation
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The Cityscapes Dataset for Semantic Urban Scene Understanding
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Deep Residual Learning for Image Recognition
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Xception: Deep Learning with Depthwise Separable Convolutions
inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-snapshot
Densely Connected Convolutional Networks
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Using GPUs for machine learning algorithms
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Deep semantic segmentation for automated driving: Taxonomy, roadmap and challenges
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
inferredcites →https://doi.org/10.1109/tpami.2016.2644615openalex-snapshot
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
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Cyclical Learning Rates for Training Neural Networks
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
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A Fast Detection Method via Region‐Based Fully Convolutional Neural Networks for Shield Tunnel Lining Defects
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Autonomous UAVs for Structural Health Monitoring Using Deep Learning and an Ultrasonic Beacon System with Geo‐Tagging
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Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images
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Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network
inferredcites →https://doi.org/10.1111/mice.12428openalex-snapshot
Foreground–background separation technique for crack detection
inferredcites →https://doi.org/10.1145/3287921.3287949openalex-snapshot
Pavement Crack Detection using Convolutional Neural Network
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AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison)
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Performance of Low-rise Reinforced Concrete Buildings in the 2016 Ecuador Earthquake
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Very Deep Convolutional Networks for Large-Scale Image Recognition
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Adam: A Method for Stochastic Optimization
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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A guide to convolution arithmetic for deep learning
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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
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Rethinking Atrous Convolution for Semantic Image Segmentation
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TensorFlow: A system for large-scale machine learning
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Rhcracknet: Refined Hierarchical Feature Fusion and Enhancement Network for Pavement Crack Detection
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Mobilecracknet:A Real-Time Crack Segmentation Network
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Automatic Crack Detection and Segmentation of Masonry Structure Based on Yolov9-Seg and Edge Detection
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Monocular Computer Vision-Based Pothole Segmentation and 3d Volume Prediction Using Advanced Deep Learning
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Loss function inversion for improved crack segmentation in steel bridges using a CNN framework
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Robotic inspection for autonomous crack segmentation and exploration using deep reinforcement learning
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Efficient instance segmentation framework for UAV-based pavement distress detection
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Deep learning and digital twin driven structural health monitoring of masonry structures: Progress and challenges
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VTECSeg: Edge-aware hybrid CNN-vision transformer network with zero-shot vision-language-guided region proposals for crack segmentation
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Diagnosis of atrial fibrillation based on lightweight detail-semantic network
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Detection of overhead line glass insulator condition using dual function device and deep learning approach
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Learning position information from attention: End-to-end weakly supervised crack segmentation with GANs
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Concrete crack detection and quantification using deep learning and structured light
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A UAV-based machine vision method for bridge crack recognition and width quantification through hybrid feature learning
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Defect segmentation: Mapping tunnel lining internal defects with ground penetrating radar data using a convolutional neural network
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Towards fully automated unmanned aerial vehicle-enabled bridge inspection: Where are we at?
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A lightweight convolutional neural network for automated crack inspection
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Quantifying sandstone crack extension and expansion via deep learning methods
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Improvement of crack detectivity for concrete surface of subway tunnels with anti-corrosion coatings using deep learning and image processing
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CA-SegFormer: An improved coordinate attention-enhanced model for crack segmentation in concrete structures
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Cracklab: A high-precision and efficient concrete crack segmentation and quantification network
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A parameter-free and effective automatic augmentation method for improved crack segmentation
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BBCNet: Boundary-body coherence network with adaptive self-attention distillation for enhanced crack segmentation
inferred← citeshttps://doi.org/10.1016/j.engappai.2021.104391openalex-snapshot
Automatic pixel-level crack segmentation in images using fully convolutional neural network based on residual blocks and pixel local weights
inferred← citeshttps://doi.org/10.1016/j.engappai.2022.105130openalex-snapshot
Crack Segmentation Network using Additive Attention Gate—CSN-II
inferred← citeshttps://doi.org/10.1016/j.engappai.2023.107507openalex-snapshot
Real-time defect detection of saw chains on automatic assembly lines based on residual networks and knowledge coding
inferred← citeshttps://doi.org/10.1016/j.engappai.2024.107976openalex-snapshot
Efficient object tracking algorithm based on lightweight Siamese networks
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SS-GAN based road surface crack region segmentation and forecasting
inferred← citeshttps://doi.org/10.1016/j.engappai.2024.108497openalex-snapshot
Unified weakly and semi-supervised crack segmentation framework using limited coarse labels
inferred← citeshttps://doi.org/10.1016/j.engappai.2024.108574openalex-snapshot
High-resolution cross-scale transformer: A deep learning model for bolt loosening detection based on monocular vision measurement
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.110302openalex-snapshot
Post-earthquake structural damage detection with tunable semi-synthetic image generation
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Dual-stage dynamic hierarchical attention framework for saliency-aware explainable diabetic retinopathy grading
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.110765openalex-snapshot
Subtle Defect Detection Network: More accurately detect subtle defects on the Printed Circuit Board surface
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.111644openalex-snapshot
A novel framework for crack segmentation using image augmentation and a CannyNet
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.112778openalex-snapshot
S 2 -AMNet: A lightweight Spatial–Spectral Adaptive Modulation Network for surface defect detection
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Crack coalescence prediction and load-bearing mechanism of defective specimen based on computer vision recognition model
inferred← citeshttps://doi.org/10.1016/j.engstruct.2025.120686openalex-snapshot
Pixel-level segmentation of spalling in masonry structures based on deep learning
inferred← citeshttps://doi.org/10.1016/j.engstruct.2025.121177openalex-snapshot
Research on intelligent diagnosis of freeze-thaw damage of concrete based on improved YOLO v8s-Segformer integrated model
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DSYOLO: A dynamic snake convolution-based segmentation model for tunnel lining cracks in complex environments
inferred← citeshttps://doi.org/10.1016/j.engstruct.2026.122159openalex-snapshot
A robust and efficient CNN-transformer network for crack segmentation of high resolution images
inferred← citeshttps://doi.org/10.1016/j.engstruct.2026.122615openalex-snapshot
A knowledge distillation framework for enhancing lightweight segmentation models with style-transferred data
inferred← citeshttps://doi.org/10.1016/j.eswa.2023.121686openalex-snapshot
Investigation on the effect of data quality and quantity of concrete cracks on the performance of deep learning-based image segmentation
inferred← citeshttps://doi.org/10.1016/j.eswa.2024.124950openalex-snapshot
A dual encoder crack segmentation network with Haar wavelet-based high–low frequency attention
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.127428openalex-snapshot
An end-to-end intelligent welding defect detection system for low-quality X-ray images with adaptive progressive learning
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.128144openalex-snapshot
HACNet V2: Rethinking the full-resolution architecture for pixel-level crack detection
inferred← citeshttps://doi.org/10.1016/j.eswa.2026.132038openalex-snapshot
Physics-data dual-driven approach for structural health monitoring: A systematic review
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DRCNet: A dual-Stream Multi-Scale information retention network for crack segmentation
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Semantic segmentation for tooth cracks using improved DeepLabv3+ model
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An interactive address matching method based on a graph attention mechanism
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CNN-based automated approach to crack-feature detection in steam cycle components
inferred← citeshttps://doi.org/10.1016/j.ijtst.2023.11.005openalex-snapshot
A state-of-the-art survey of deep learning models for automated pavement crack segmentation
inferred← citeshttps://doi.org/10.1016/j.imavis.2026.106078openalex-snapshot
LDAC-Net: A parallel dual-encoder network with Cross-Gated Residual Fusion for pavement crack segmentation
inferred← citeshttps://doi.org/10.1016/j.infrared.2024.105241openalex-snapshot
Quantitative identification of debonding defects in building façades based on UAV-thermography using a two-stage network integrating dual attention mechanism
inferred← citeshttps://doi.org/10.1016/j.inpa.2024.03.002openalex-snapshot
Innovative deep learning approach for cross-crop plant disease detection: A generalized method for identifying unhealthy leaves
inferred← citeshttps://doi.org/10.1016/j.istruc.2023.05.062openalex-snapshot
Surface concrete cracks detection and segmentation using transfer learning and multi-resolution image processing
inferred← citeshttps://doi.org/10.1016/j.istruc.2024.107073openalex-snapshot
DCNAM: Automatic detection of pixel level fine crack using a densely connected network with attention mechanism
inferred← citeshttps://doi.org/10.1016/j.istruc.2025.108850openalex-snapshot
Automatic crack detection and segmentation of masonry structure based on deep learning network and edge detection
inferred← citeshttps://doi.org/10.1016/j.istruc.2025.110849openalex-snapshot
A bridge crack segmentation method based on full attention feature integration and rendering
inferred← citeshttps://doi.org/10.1016/j.istruc.2026.111701openalex-snapshot
Automatic identification, tracking and 3D quantification of structure crack initiation and propagation
inferred← citeshttps://doi.org/10.1016/j.istruc.2026.111811openalex-snapshot
CrackAwareNet: A continuity-aware lightweight network for fine crack segmentation
inferred← citeshttps://doi.org/10.1016/j.jag.2025.105048openalex-snapshot
High–low frequency feature fusion network for pavement crack segmentation in complex environments
inferred← citeshttps://doi.org/10.1016/j.jfca.2025.108082openalex-snapshot
Automated 3D wheat tissue analysis using x-ray CT and deep learning
inferred← citeshttps://doi.org/10.1016/j.jii.2022.100403openalex-snapshot
EGD-Net: Edge-guided and differential attention network for surface defect detection
inferred← citeshttps://doi.org/10.1016/j.jobe.2024.110814openalex-snapshot
An iterative up-sampling convolutional neural network for glass curtain crack detection using unmanned aerial vehicles
inferred← citeshttps://doi.org/10.1016/j.jobe.2026.116609openalex-snapshot
Label-efficient segmentation of concrete surface defects via multi-level consistency regularization
inferred← citeshttps://doi.org/10.1016/j.jtte.2022.11.003openalex-snapshot
A review of deep learning methods for pixel-level crack detection
inferred← citeshttps://doi.org/10.1016/j.kscej.2025.100410openalex-snapshot
Deep learning-based defect identification in hydraulic structures: A comprehensive review
inferred← citeshttps://doi.org/10.1016/j.matcom.2025.02.003openalex-snapshot
A digital twin approach based method in civil engineering for classification of salt damage in building evaluation
inferred← citeshttps://doi.org/10.1016/j.meadig.2026.100040openalex-snapshot
Deep learning-based crack detection and 3D reconstruction for cost-effective structural health monitoring
inferred← citeshttps://doi.org/10.1016/j.measurement.2022.111550openalex-snapshot
Automatic segmentation and quantification of global cracks in concrete structures based on deep learning
inferred← citeshttps://doi.org/10.1016/j.measurement.2023.112892openalex-snapshot
A hybrid lightweight encoder-decoder network for automatic bridge crack assessment with real-world interference
inferred← citeshttps://doi.org/10.1016/j.measurement.2024.116639openalex-snapshot
PIndNet: A pixel-wise industrial defect inspection network using multiple pyramid feature aggregation
inferred← citeshttps://doi.org/10.1016/j.measurement.2025.117493openalex-snapshot
A rapid and precise detection and measurement method for road cracks based on video keyframes and DPPNet
inferred← citeshttps://doi.org/10.1016/j.measurement.2026.120820openalex-snapshot
Classification and information extraction of road crack images based on multimodal large language models
inferred← citeshttps://doi.org/10.1016/j.patcog.2025.111723openalex-snapshot
Crack segmentation network via difference convolution-based encoder and hybrid CNN-Mamba multi-scale attention
inferred← citeshttps://doi.org/10.1016/j.procs.2022.09.457openalex-snapshot
A Lightweight CNN-Based Vision System for Concrete Crack Detection on a Low-Power Embedded Microcontroller Platform
inferred← citeshttps://doi.org/10.1016/j.renene.2024.122332openalex-snapshot
Digital twin of wind turbine surface damage detection based on deep learning-aided drone inspection
inferred← citeshttps://doi.org/10.1016/j.rineng.2023.101267openalex-snapshot
Automatic pavement crack segmentation using a generative adversarial network (GAN)-based convolutional neural network
inferred← citeshttps://doi.org/10.1016/j.rineng.2025.108864openalex-snapshot
U-Net driven semantic segmentation for detection and quantification of cracks on gas turbine blade tips
inferred← citeshttps://doi.org/10.1016/j.tust.2021.103949openalex-snapshot
Deep learning based method of longitudinal dislocation detection for metro shield tunnel segment
inferred← citeshttps://doi.org/10.1016/j.tust.2022.104403openalex-snapshot
Automatic sewer defect detection and severity quantification based on pixel-level semantic segmentation
inferred← citeshttps://doi.org/10.1016/j.tust.2022.104472openalex-snapshot
Automatic tunnel lining crack evaluation and measurement using deep learning
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Batched-image detection model and deployment method for tunnel lining defects using line-scan cameras based on experiments study
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