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Guoyu Tong, Changgang Wang, Jiajun Feng, Nanzi Su, Weirong Xu, Hui Yan, Haonan Chen
Abstract
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Machine learning-enhanced optical monitoring for identifying pitting-susceptible zones in 316L stainless steel
10.1016/j.corsci.2025.113184 · 2025
Deep learning AI for corrosion detection
2019
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Thinning evaluation of steel plates for weathering tests based on convolutional neural networks
Provenance
crossref
Confidence 100%
ror
Confidence 99%
ror
Confidence 99%
openalex
Confidence 95%
datacite
Confidence 0%
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Applying fully convolutional neural networks for corrosion semantic segmentation for steel bridges: the use of U-Net
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Real-time monitoring of the corrosion behaviour of the 304SS in HCl solution using BPNN with joint image recognition and electrochemical noise
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Atmospheric corrosion of Ni-advanced weathering steels in marine atmospheres of moderate salinity
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Corrosion big-data driven continuous observation of low alloy steel rust layer evolution and mining of influence rules of atmospheric environment interaction
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Insights into atmospheric corrosion evolution of weathering steels with different nickel contents during simulated coastal atmospheric wet-dry cycling
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Prediction of quantitative in-situ local corrosion via deep learning
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Quantitative prediction of Mg-RE-Ni alloy corrosion behavior by machine learning
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Artificial intelligence combined with high-throughput calculations to improve the corrosion resistance of AlMgZn alloy
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Deep learning for traffic scene understanding: a review
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A classification method embedding atypical patterns for distinguishing tumor subtypes in PET/CT images
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A lightweight network for contextual and morphological awareness for hepatic vein segmentation
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Multi-threshold deep metric learning for facial expression recognition
10.1016/j.patcog.2024.110711 · doi-reference
Benchmarking probabilistic deep learning methods for license plate recognition
10.1109/tits.2023.3278533 · doi-reference
Deep learning-powered visual inspection for metal surfaces – impact of annotations on algorithms based on defect characteristics
10.1016/j.aei.2024.102727 · doi-reference
Developing an explainable hybrid deep learning model in digital transformation: an empirical study
10.1007/s10845-023-02127-y · doi-reference
UniFormer: unifying convolution and self-attention for visual recognition
10.1109/tpami.2023.3282631 · doi-reference
MultiScaleCrackNet: a parallel multiscale deep CNN architecture for concrete crack classification
10.1016/j.eswa.2024.123658 · doi-reference
Cross-dimensional transfer learning in medical image segmentation with deep learning
10.1016/j.media.2023.102868 · doi-reference
YOGA: deep object detection in the wild with lightweight feature learning and multiscale attention
10.1016/j.patcog.2023.109451 · doi-reference
A review of convolutional neural networks in computer vision
10.1007/s10462-024-10721-6 · doi-reference
A survey of multimodal hybrid deep learning for computer vision: architectures, applications, trends, and challenges
10.1016/j.inffus.2023.102217 · doi-reference
Insight into atmospheric corrosion evolution of mild steel in a simulated coastal atmosphere
10.1016/j.jmst.2020.11.021 · doi-reference
Evolution of corrosion of MnCuP weathering steel submitted to wet/dry cyclic tests in a simulated coastal atmosphere
10.1016/j.corsci.2012.01.017 · doi-reference
Insight to atmosphere corrosion behavior of Q345NH steel in Wenchang tropical marine environment
10.1016/j.jmrt.2023.04.128 · doi-reference
Insights into atmospheric corrosion evolution of weathering steels with different nickel contents during simulated coastal atmospheric wet-dry cycling
10.1016/j.corcom.2025.02.003 · doi-reference
Corrosion big-data driven continuous observation of low alloy steel rust layer evolution and mining of influence rules of atmospheric environment interaction
10.1016/j.corsci.2025.113117 · doi-reference
Atmospheric corrosion of Ni-advanced weathering steels in marine atmospheres of moderate salinity
10.1016/j.corsci.2013.06.053 · doi-reference
Assessment and prediction of corrosion damage of Q355 ordinary steel and Q690 high-strength steel in simulated coastal atmospheric environment
10.1016/j.istruc.2026.111962 · doi-reference
Study on the corrosion behavior of three types of steel in marine atmospheric environment
10.1016/j.ijoes.2026.101298 · doi-reference
Explainable artificial intelligence for visual fingerprinting of copper tubes’ atmospheric corrosion in diverse environments
10.1016/j.corsci.2026.113670 · doi-reference
Failure analysis and corrosion prediction of scratched coating/steel system in marine atmosphere using semantic segmentation and temporal prediction
10.1016/j.engfailanal.2025.109912 · doi-reference
Multi-scale corrosion degradation of aluminum alloys in marine environment studied by computational, experimental, and artificial intelligence-assisted approaches
10.1016/j.pmatsci.2026.101740 · doi-reference
Real-time monitoring of the corrosion behaviour of the 304SS in HCl solution using BPNN with joint image recognition and electrochemical noise
10.1016/j.corsci.2023.111779 · doi-reference
Thinning evaluation of steel plates for weathering tests based on convolutional neural networks
10.5006/3674 · doi-reference
Advanced multi-image segmentation-based machine learning modeling strategy for corrosion prediction and rust layer performance evaluation of weathering steel
10.1016/j.corsci.2024.112334 · doi-reference
Corrosion grade recognition for weathering steel plate based on a convolutional neural network
10.1088/1361-6501/ac7034 · doi-reference
Review-material degradation assessed by digital image processing: fundamentals, progresses, and challenges
10.1016/j.jmst.2020.04.033 · doi-reference
Atmospheric corrosion assessed from corrosion images using fuzzy Kolmogorov–Sinai entropy
10.1016/j.corsci.2017.02.015 · doi-reference
Early-stage forecasting of bronze disease development with chlorine mapping: integrating computer vision and multimodal characterization methodologies
10.1016/j.corsci.2025.113403 · doi-reference
Evaluation of image segmentation approaches for non-destructive detection and quantification of corrosion damage on stonework
10.1016/j.corsci.2007.03.049 · doi-reference
Recent advancements in end-to-end autonomous driving using deep learning: a survey
10.1109/tiv.2023.3318070 · doi-reference
Machine learning-enhanced optical monitoring for identifying pitting-susceptible zones in 316L stainless steel
10.1016/j.corsci.2025.113184 · doi-reference