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Automatic detection of welding defects using the convolutional neural network

  • Published:2019-06-21 Issue: Volume: Page:
  • ISSN:
  • Container-title:Automated Visual Inspection and Machine Vision III
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Author:

Sizyakin Roman,Voronin Viacheslav V.,Gapon Nikolay,Zelensky Aleksandr,Pižurica Aleksandra

Publisher

SPIE

Reference26 articles.

1. Imagenet large scale visual recognition challenge;Russakovsky;CoRR abs/1409.0575,2014

2. Automatic detection of welding defects using texture features

3. Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters

4. Textural Features for Image Classification

5. An introduction to kernel and nearest-neighbor nonparametric regression;Altman;The American Statistician,1992

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1. Computer Vision for Safety Management in the Steel Industry;AI;2024-07-19

2. Automated Welding Defect Recognition through Deep Learning Fusion: CNN and SVM Integration;2023 4th International Conference on Intelligent Technologies (CONIT);2024-06-21

3. Casting Defect Forecasting with Integrated Convolutional Neural Networks and Random Forest;2024 International Conference on Intelligent Systems for Cybersecurity (ISCS);2024-05-03

4. Detection of Weld Defects in Patch-based Radiographic Images Using Deep Learning;2024 8th International Conference on Image and Signal Processing and their Applications (ISPA);2024-04-21

5. Welding Processes and Common Defects: A Comprehensive Overview;2023 2nd International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS);2023-11-01
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