Research graph
References from Interpretable multi-task learning for corrosion state prediction of carbon steel in simulated marine atmospheric environments from rust morphology. Local targets link to admitted publications; unresolved targets remain external evidence.
Machine learning-enhanced optical monitoring for identifying pitting-susceptible zones in 316L stainless steel
10.1016/j.corsci.2025.113184 · 2025 · External reference
Deep learning AI for corrosion detection
2019 · External reference
Evaluation of image segmentation approaches for non-destructive detection and quantification of corrosion damage on stonework
10.1016/j.corsci.2007.03.049 · 2007 · External reference
Early-stage forecasting of bronze disease development with chlorine mapping: integrating computer vision and multimodal characterization methodologies
10.1016/j.corsci.2025.113403 · 2026 · External reference
Atmospheric corrosion assessed from corrosion images using fuzzy Kolmogorov–Sinai entropy
10.1016/j.corsci.2017.02.015 · 2017 · External reference
Review-material degradation assessed by digital image processing: fundamentals, progresses, and challenges
10.1016/j.jmst.2020.04.033 · 2020 · External reference
Corrosion grade recognition for weathering steel plate based on a convolutional neural network
10.1088/1361-6501/ac7034 · 2022 · External 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 · 2024 · External reference
Thinning evaluation of steel plates for weathering tests based on convolutional neural networks
10.5006/3674 · 2021 · External reference
Applying fully convolutional neural networks for corrosion semantic segmentation for steel bridges: the use of U-Net
2021 · External 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 · 2024 · External 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 · 2026 · External 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 · 2025 · External reference
Explainable artificial intelligence for visual fingerprinting of copper tubes’ atmospheric corrosion in diverse environments
10.1016/j.corsci.2026.113670 · 2026 · External reference
Study on the corrosion behavior of three types of steel in marine atmospheric environment
10.1016/j.ijoes.2026.101298 · 2026 · External 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 · 2026 · External reference
Atmospheric corrosion of Ni-advanced weathering steels in marine atmospheres of moderate salinity
10.1016/j.corsci.2013.06.053 · 2013 · External 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 · 2025 · External 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 · 2026 · External reference
Insight to atmosphere corrosion behavior of Q345NH steel in Wenchang tropical marine environment
10.1016/j.jmrt.2023.04.128 · 2023 · External 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 · 2012 · External reference
Insight into atmospheric corrosion evolution of mild steel in a simulated coastal atmosphere
10.1016/j.jmst.2020.11.021 · 2021 · External reference
A survey of multimodal hybrid deep learning for computer vision: architectures, applications, trends, and challenges
10.1016/j.inffus.2023.102217 · 2024 · External reference
A review of convolutional neural networks in computer vision
10.1007/s10462-024-10721-6 · 2024 · External reference
YOGA: deep object detection in the wild with lightweight feature learning and multiscale attention
10.1016/j.patcog.2023.109451 · 2023 · External reference
Cross-dimensional transfer learning in medical image segmentation with deep learning
10.1016/j.media.2023.102868 · 2023 · External reference
MultiScaleCrackNet: a parallel multiscale deep CNN architecture for concrete crack classification
10.1016/j.eswa.2024.123658 · 2024 · External reference
UniFormer: unifying convolution and self-attention for visual recognition
10.1109/tpami.2023.3282631 · 2023 · External reference
Developing an explainable hybrid deep learning model in digital transformation: an empirical study
10.1007/s10845-023-02127-y · 2024 · External reference
Multi-scale semantic and detail extraction network for lightweight person re-identification
10.1016/j.cviu.2023.103813 · 2023 · External reference
Recent advancements in end-to-end autonomous driving using deep learning: a survey
10.1109/tiv.2023.3318070 · 2024 · External reference
Deep learning-powered visual inspection for metal surfaces – impact of annotations on algorithms based on defect characteristics
10.1016/j.aei.2024.102727 · 2024 · External reference
Benchmarking probabilistic deep learning methods for license plate recognition
10.1109/tits.2023.3278533 · 2023 · External reference
Multi-threshold deep metric learning for facial expression recognition
10.1016/j.patcog.2024.110711 · 2024 · External reference
A lightweight network for contextual and morphological awareness for hepatic vein segmentation
10.1109/jbhi.2023.3305644 · 2023 · External reference
A classification method embedding atypical patterns for distinguishing tumor subtypes in PET/CT images
10.1016/j.bspc.2024.106663 · 2024 · External reference
Deep learning for traffic scene understanding: a review
10.1109/access.2025.3529289 · 2025 · External reference
Artificial intelligence combined with high-throughput calculations to improve the corrosion resistance of AlMgZn alloy
10.1016/j.corsci.2024.112062 · 2024 · External reference
Quantitative prediction of Mg-RE-Ni alloy corrosion behavior by machine learning
10.1016/j.corsci.2024.112324 · 2024 · External reference
Prediction of quantitative in-situ local corrosion via deep learning
10.1016/j.corsci.2024.112431 · 2024 · External reference
Nano-porosity effects on corrosion rate of Zr alloys using nanoscale microscopy coupled to machine learning
10.1016/j.corsci.2022.110660 · 2022 · External reference
Cross-category prediction of corrosion inhibitor performance based on molecular graph structures via a three-level message passing neural network model
10.1016/j.corsci.2022.110780 · 2022 · External reference
Data-driven corrosion inhibition efficiency prediction model incorporating 2D–3D molecular graphs and inhibitor concentration
10.1016/j.corsci.2023.111420 · 2023 · External reference
Deep residual learning for image recognition
2016 · External reference
Squeeze-and-excitation networks
2018 · External reference
Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
2018 · External reference
Beyond losses reweighting: empowering multi-task learning via the generalization perspective
2026 · External reference
Multi-task learning for dense prediction tasks: a survey
2021 · External reference
A review of convolutional neural networks in computer vision
10.1007/s10462-024-10721-6 · ExternalCitation · doi-reference
Developing an explainable hybrid deep learning model in digital transformation: an empirical study
10.1007/s10845-023-02127-y · ExternalCitation · 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 · ExternalCitation · doi-reference
A classification method embedding atypical patterns for distinguishing tumor subtypes in PET/CT images
10.1016/j.bspc.2024.106663 · ExternalCitation · 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 · ExternalCitation · 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 · ExternalCitation · 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 · ExternalCitation · doi-reference
Atmospheric corrosion of Ni-advanced weathering steels in marine atmospheres of moderate salinity
10.1016/j.corsci.2013.06.053 · ExternalCitation · doi-reference
Atmospheric corrosion assessed from corrosion images using fuzzy Kolmogorov–Sinai entropy
10.1016/j.corsci.2017.02.015 · ExternalCitation · doi-reference
Nano-porosity effects on corrosion rate of Zr alloys using nanoscale microscopy coupled to machine learning
10.1016/j.corsci.2022.110660 · ExternalCitation · doi-reference
Cross-category prediction of corrosion inhibitor performance based on molecular graph structures via a three-level message passing neural network model
10.1016/j.corsci.2022.110780 · ExternalCitation · doi-reference
Data-driven corrosion inhibition efficiency prediction model incorporating 2D–3D molecular graphs and inhibitor concentration
10.1016/j.corsci.2023.111420 · ExternalCitation · 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 · ExternalCitation · doi-reference
Artificial intelligence combined with high-throughput calculations to improve the corrosion resistance of AlMgZn alloy
10.1016/j.corsci.2024.112062 · ExternalCitation · doi-reference
Quantitative prediction of Mg-RE-Ni alloy corrosion behavior by machine learning
10.1016/j.corsci.2024.112324 · ExternalCitation · 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 · ExternalCitation · doi-reference
Prediction of quantitative in-situ local corrosion via deep learning
10.1016/j.corsci.2024.112431 · ExternalCitation · 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 · ExternalCitation · doi-reference
Machine learning-enhanced optical monitoring for identifying pitting-susceptible zones in 316L stainless steel
10.1016/j.corsci.2025.113184 · ExternalCitation · 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 · ExternalCitation · doi-reference
Explainable artificial intelligence for visual fingerprinting of copper tubes’ atmospheric corrosion in diverse environments
10.1016/j.corsci.2026.113670 · ExternalCitation · doi-reference
Multi-scale semantic and detail extraction network for lightweight person re-identification
10.1016/j.cviu.2023.103813 · ExternalCitation · 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 · ExternalCitation · doi-reference
MultiScaleCrackNet: a parallel multiscale deep CNN architecture for concrete crack classification
10.1016/j.eswa.2024.123658 · ExternalCitation · doi-reference
Study on the corrosion behavior of three types of steel in marine atmospheric environment
10.1016/j.ijoes.2026.101298 · ExternalCitation · doi-reference
A survey of multimodal hybrid deep learning for computer vision: architectures, applications, trends, and challenges
10.1016/j.inffus.2023.102217 · ExternalCitation · 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 · ExternalCitation · doi-reference
Insight to atmosphere corrosion behavior of Q345NH steel in Wenchang tropical marine environment
10.1016/j.jmrt.2023.04.128 · ExternalCitation · doi-reference
Review-material degradation assessed by digital image processing: fundamentals, progresses, and challenges
10.1016/j.jmst.2020.04.033 · ExternalCitation · doi-reference
Insight into atmospheric corrosion evolution of mild steel in a simulated coastal atmosphere
10.1016/j.jmst.2020.11.021 · ExternalCitation · doi-reference
Cross-dimensional transfer learning in medical image segmentation with deep learning
10.1016/j.media.2023.102868 · ExternalCitation · doi-reference
YOGA: deep object detection in the wild with lightweight feature learning and multiscale attention
10.1016/j.patcog.2023.109451 · ExternalCitation · doi-reference
Multi-threshold deep metric learning for facial expression recognition
10.1016/j.patcog.2024.110711 · ExternalCitation · 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 · ExternalCitation · doi-reference
Corrosion grade recognition for weathering steel plate based on a convolutional neural network
10.1088/1361-6501/ac7034 · ExternalCitation · doi-reference
Deep learning for traffic scene understanding: a review
10.1109/access.2025.3529289 · ExternalCitation · doi-reference
A lightweight network for contextual and morphological awareness for hepatic vein segmentation
10.1109/jbhi.2023.3305644 · ExternalCitation · doi-reference
Benchmarking probabilistic deep learning methods for license plate recognition
10.1109/tits.2023.3278533 · ExternalCitation · doi-reference
Recent advancements in end-to-end autonomous driving using deep learning: a survey
10.1109/tiv.2023.3318070 · ExternalCitation · doi-reference
UniFormer: unifying convolution and self-attention for visual recognition
10.1109/tpami.2023.3282631 · ExternalCitation · doi-reference
Thinning evaluation of steel plates for weathering tests based on convolutional neural networks
10.5006/3674 · ExternalCitation · doi-reference
