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Steel surface defects analysis with machine vision and deep learning

https://doi.org/10.1007/s00170-025-16539-y
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What this work says

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

inferredcites →https://doi.org/10.1002/srin.201400016openalex-snapshot
Use of Artificial Intelligence in Classification of Mill Scale Defects
inferredcites →https://doi.org/10.1002/srin.201600068openalex-snapshot
An End‐to‐End Steel Strip Surface Defects Recognition System Based on Convolutional Neural Networks
inferredcites →https://doi.org/10.1007/978-1-349-13049-8openalex-snapshot
Applied Image Processing
inferredcites →https://doi.org/10.1007/978-1-4842-6168-2openalex-snapshot
Convolutional Neural Networks with Swift for Tensorflow
inferredcites →https://doi.org/10.1007/s00170-012-4556-7openalex-snapshot
Occurrence of surface defects on strips during hot rolling process by FEM
inferredcites →https://doi.org/10.1007/s00170-023-11087-9openalex-snapshot
FDD: a deep learning–based steel defect detectors
inferredcites →https://doi.org/10.1007/s10462-023-10475-7openalex-snapshot
A survey of real-time surface defect inspection methods based on deep learning
inferredcites →https://doi.org/10.1007/s10845-013-0834-yopenalex-snapshot
On line prediction of surface defects in hot bar rolling based on Bayesian hierarchical modeling
inferredcites →https://doi.org/10.1007/s10845-020-01670-2openalex-snapshot
A steel surface defect inspection approach towards smart industrial monitoring
inferredcites →https://doi.org/10.1007/s10845-021-01754-7openalex-snapshot
Machine learning in continuous casting of steel: a state-of-the-art survey
inferredcites →https://doi.org/10.1007/s10853-017-0831-1openalex-snapshot
Influence of inclusion type on the very high cycle fatigue properties of 18Ni maraging steel
inferredcites →https://doi.org/10.1007/s12541-013-0232-6openalex-snapshot
A study to predict the creation of surface defects on material and suppress them in caliber rolling process
inferredcites →https://doi.org/10.1016/b978-0-12-813876-2.00006-6openalex-snapshot
Effects of nonmetallic inclusions on fatigue strength
inferredcites →https://doi.org/10.1016/j.apsusc.2012.03.007openalex-snapshot
A local annular contrast based real-time inspection algorithm for steel bar surface defects
inferredcites →https://doi.org/10.1016/j.apsusc.2013.09.002openalex-snapshot
A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects
inferredcites →https://doi.org/10.1016/j.engappai.2022.105553openalex-snapshot
On enhancing prediction abilities of vision-based metallic surface defect classification through adversarial training
inferredcites →https://doi.org/10.1016/j.engappai.2023.106517openalex-snapshot
Defect engineering of fatigue-resistant steels by data-driven models
inferredcites →https://doi.org/10.1016/j.engappai.2023.107717openalex-snapshot
A systematic review of deep learning approaches for surface defect detection in industrial applications
inferredcites →https://doi.org/10.1016/j.engappai.2024.108045openalex-snapshot
Research on X-ray weld seam defect detection and size measurement method based on neural network self-optimization
inferredcites →https://doi.org/10.1016/j.engappai.2024.109289openalex-snapshot
A novel real-time steel surface defect detection method with enhanced feature extraction and adaptive fusion
inferredcites →https://doi.org/10.1016/j.engappai.2024.109325openalex-snapshot
An efficient detector for detecting surface defects on cold-rolled steel strips
inferredcites →https://doi.org/10.1016/j.engappai.2024.109469openalex-snapshot
Two-stage encoder multi-decoder network with global–local up-sampling for defect segmentation of strip steel surface defects
inferredcites →https://doi.org/10.1016/j.engfailanal.2005.04.009openalex-snapshot
Manufacturing defects and the evidence of thermomechanical fatigue in a ceramic vacuum furnace tube
inferredcites →https://doi.org/10.1016/j.engfailanal.2022.106047openalex-snapshot
Visual techniques for defects detection in steel products: A comparative study
inferredcites →https://doi.org/10.1016/j.engfailanal.2022.106630openalex-snapshot
Effect of manufacturing defects on fatigue life of high strength steel bolts for wind turbines
inferredcites →https://doi.org/10.1016/j.engfailanal.2023.107592openalex-snapshot
Fatigue failure of centrifugal compressor impellers: A comprehensive review
inferredcites →https://doi.org/10.1016/j.eswa.2010.12.026openalex-snapshot
Process knowledge based multi-class support vector classification (PK-MSVM) approach for surface defects in hot rolling
inferredcites →https://doi.org/10.1016/j.ijfatigue.2011.12.001openalex-snapshot
Material defects as the basis of fatigue design
inferredcites →https://doi.org/10.1016/j.ijfatigue.2013.06.017openalex-snapshot
Influence of microstructure and surface defect on very high cycle fatigue properties of clean spring steel
inferredcites →https://doi.org/10.1016/j.ijfatigue.2016.01.013openalex-snapshot
Interaction between a surface defect and grain size under high cycle fatigue loading: Experimental approach for Armco iron
inferredcites →https://doi.org/10.1016/j.ijfatigue.2016.05.032openalex-snapshot
Very-high-cycle fatigue behavior of a structural steel with and without induced surface defects
inferredcites →https://doi.org/10.1016/j.ijfatigue.2019.06.021openalex-snapshot
Effect of small defects on the fatigue strength of martensitic stainless steels
inferredcites →https://doi.org/10.1016/j.mfglet.2022.10.001openalex-snapshot
NSLNet: An improved deep learning model for steel surface defect classification utilizing small training datasets
inferredcites →https://doi.org/10.1016/j.neucom.2015.05.134openalex-snapshot
Surface defect classification in large-scale strip steel image collection via hybrid chromosome genetic algorithm
inferredcites →https://doi.org/10.1016/j.optlaseng.2019.05.005openalex-snapshot
A deep-learning-based approach for fast and robust steel surface defects classification
inferredcites →https://doi.org/10.1016/j.optlastec.2012.05.016openalex-snapshot
A lower envelope Weber contrast detection algorithm for steel bar surface pit defects
inferredcites →https://doi.org/10.1016/j.rcim.2015.09.008openalex-snapshot
A cost-effective and automatic surface defect inspection system for hot-rolled flat steel
inferredcites →https://doi.org/10.1049/ipr2.12647openalex-snapshot
Review of surface defect detection of steel products based on machine vision
inferredcites →https://doi.org/10.1109/cvpr.2018.00474openalex-snapshot
MobileNetV2: Inverted Residuals and Linear Bottlenecks
inferredcites →https://doi.org/10.1109/tcyb.2017.2668395openalex-snapshot
A Generic Deep-Learning-Based Approach for Automated Surface Inspection
inferredcites →https://doi.org/10.1109/tim.2012.2218677openalex-snapshot
Automatic Defect Detection on Hot-Rolled Flat Steel Products
inferredcites →https://doi.org/10.1109/tim.2021.3125987openalex-snapshot
An Adaptive Pyramid Graph and Variation Residual-Based Anomaly Detection Network for Rail Surface Defects
inferredcites →https://doi.org/10.1109/tim.2023.3282301openalex-snapshot
Semantic Segmentation of Metal Surface Defects and Corresponding Strategies
inferredcites →https://doi.org/10.1145/1281500.1281604openalex-snapshot
A gentle introduction to bilateral filtering and its applications
inferredcites →https://doi.org/10.1186/1687-5281-2014-50openalex-snapshot
Review of vision-based steel surface inspection systems
inferredcites →https://doi.org/10.3390/coatings13010017openalex-snapshot
Steel Surface Defect Recognition: A Survey
inferredcites →https://doi.org/10.3390/s20185136openalex-snapshot
Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials

What points at this work

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

inferred← backgroundhttps://doi.org/10.5281/zenodo.17210929zenodo-depositor-declared
inferred← citeshttps://doi.org/10.1002/srin.202501291openalex-snapshot
Predicting and Analyzing the Mechanical Properties of DP980 (Ferrite + Martensite) Steel by Visualizing Based on Deep Learning
inferred← citeshttps://doi.org/10.1007/s00170-026-17658-wopenalex-snapshot
An automated optical inspection system embedded with Bayesian optimization and support vector data description for imbalanced micro-LED defects
inferred← citeshttps://doi.org/10.1109/aipcvt67868.2025.11405554openalex-snapshot
Surface defect detection algorithm of aero-engine blade based on improved YOLO11n model
inferred← citeshttps://doi.org/10.1109/cicse70286.2026.11620970openalex-snapshot
The cross-scale edge-aware network improves the pixel-level localization accuracy of PCB solder joint defects
inferred← citeshttps://doi.org/10.1145/3839242openalex-snapshot
PipeDet: Pipe Detection in the Complex Structured Environment of Aeroengine Outer Profile
inferred← citeshttps://doi.org/10.1186/s44147-026-00954-3openalex-snapshot
Design and implementation of a machine learning-integrated reliable quality control evaluation system for cigarette manufacturing process
inferred← citeshttps://doi.org/10.21203/rs.3.rs-8042276/v1openalex-snapshot
AI-enabled Monitoring of Planar-Flow Casting of Amorphous Steel Foils
inferred← citeshttps://doi.org/10.3390/en19204839openalex-snapshot
Resource-Adaptive Neuro-Fuzzy Edge System for Wind Turbine Blade Defect Diagnosis
inferred← citeshttps://doi.org/10.3390/s26144360openalex-snapshot
DAMF-Net: A Dynamic Receptive Field Enhancement and Semantic-Guided Adaptive Modulated Fusion Network for Steel Surface Defect Segmentation
inferred← citeshttps://doi.org/10.3390/technologies14100644openalex-snapshot
A Review of Steel Surface Defect Detection for Industrial Applications: Evaluation Frameworks, Data Challenges, and Model Mechanisms
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