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Quality assessment methodology based on machine learning with small datasets: Industrial castings defects

  • Published:2021-10 Issue: Volume:456 Page:622-628
  • ISSN:0925-2312
  • Container-title:Neurocomputing
  • language:en
  • Short-container-title:Neurocomputing

Author:

Pastor-López Iker,Sanz Borja,Tellaeche Alberto,Psaila Giuseppe,de la Puerta José Gaviria,Bringas Pablo G.

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference25 articles.

1. Deep learning;LeCun;Nature,2015

2. Deep learning for computer vision: A brief review;Voulodimos;Comput. Intell. Neurosci.,2018

3. Deep residual learning for image recognition;He,2016

4. Object detection binary classifiers methodology based on deep learning to identify small objects handled similarly: Application in video surveillance;Pérez-Hernández;Knowl.-Based Syst.,2020

5. A survey of deep neural network architectures and their applications;Liu;Neurocomputing,2017

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1. DES-YOLO: a novel model for real-time detection of casting surface defects;PeerJ Computer Science;2024-08-22

2. Hybrid learning integration of iterative weighted least squares and backpropagation neural networks for advanced manufacturing analysis;The International Journal of Advanced Manufacturing Technology;2024-08-08

3. Exploring deep fully convolutional neural networks for surface defect detection in complex geometries;The International Journal of Advanced Manufacturing Technology;2024-07-16

4. Predictive Modeling for Mold Material Defects Using CNN-Random Forest Fusion;2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS);2024-06-28

5. The Using of Artificial Neural Networks for Reinforced Concrete Structures Condition Monitoring;2024 International Russian Smart Industry Conference (SmartIndustryCon);2024-03-25
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