Reference health
Deep Learning-Based Method for Detection and Feature Quantification of Microscopic Cracks on the Surface of Concrete Dams
14/14 checkable references clean · checked 2026-09-17
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
31 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 14 checked references that resolve
resolves10.1016/j.measurement.2021.109137
Multi-sensing investigation of crack problems for concrete dams based on detection and monitoring data: A case study
resolves10.1007/s11042-018-5880-1
A novel automatic dam crack detection algorithm based on local-global clustering
resolves10.1007/s11831-022-09845-1
Evolutionary Computation Modelling for Structural Health Monitoring of Critical Infrastructure
resolves10.1061/(ASCE)CP.1943-5487.0000918
Machine Learning for Crack Detection: Review and Model Performance Comparison
resolves10.3390/rs71114680
Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery
resolves10.1016/j.conbuildmat.2022.129238
Review on computer vision-based crack detection and quantification methodologies for civil structures
resolves10.1007/s40747-022-00876-6
Automated bridge crack detection method based on lightweight vision models
resolves10.1007/s13349-023-00684-7
A real-time multi-defect automatic identification framework for concrete dams via improved YOLOv5 and knowledge distillation
resolves10.3390/s20072069
Automatic Pixel-Level Crack Detection on Dam Surface Using Deep Convolutional Network
resolves10.1016/j.neucom.2022.07.036
An underwater dam crack image segmentation method based on multi-level adversarial transfer learning
resolves10.3390/s23167190
UAV-YOLOv8: A Small-Object-Detection Model Based on Improved YOLOv8 for UAV Aerial Photography Scenarios
The 31 references without a DOI — listed, not checked
no DOI — not checkedref1
no DOI — not checkedInspection and Treatment of Surface Cracks on Upstream Face of Danjiangkou Initial Project Dam[J]
no DOI — not checkedA Method for Dam Surface Crack Detection Based on Improved DeepLabV3+ Network
no DOI — not checkedref7
no DOI — not checkedFeasibility Study on Identification of Bridge Crack Width Based on Unmanned Aerial Vehicle Imaging
no DOI — not checkedref9
no DOI — not checkedref10
no DOI — not checkedA review of machine vision-based structural health monitoring: Methodologies and applications[J]
no DOI — not checkedref12
no DOI — not checkedref13
no DOI — not checkedref19
no DOI — not checkedEnhanced precision in dam crack width measurement: Leveraging advanced lightweight network identification for pixel-level accuracy[J]
no DOI — not checkedref23
no DOI — not checkedReal-Time Detection Method for Concrete Dam Cracks Based on Object Detection[J/OL]
no DOI — not checkedref25
no DOI — not checkedref28
no DOI — not checkedSemantic Segmentation Method for Crack Detection in Hydraulic Structures Based on Feature
no DOI — not checkedref31
no DOI — not checkedDynamic snake convolution based on topological geometric constraints for tubular structure segmentation
no DOI — not checkedBridge Crack Detection Based on Improved DeeplabV3+ and Transfer Learning[J]
no DOI — not checkedEfficient multi-scale attention module with cross-spatial learning
no DOI — not checkedref35
no DOI — not checkedref36
no DOI — not checkedU-net: Convolutional networks for biomedical image segmentation
no DOI — not checkedref38
no DOI — not checkedEncoder-decoder with atrous separable convolution for semantic image segmentation
no DOI — not checkedDynamically pruning segformer for efficient semantic segmentation
no DOI — not checkedref41
no DOI — not checkedFaster r-cnn: Towards real-time object detection with region proposal networks[J]
no DOI — not checkedref44
no DOI — not checkedTrainable bag-of-freebies sets new stateof-the-art for real-time object detectors
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