Image Data Augmentation Approaches: A Comprehensive Survey and Future Directions
Deep learning algorithms have exhibited impressive performance across various computer vision tasks; however, the challenge of overfitting persists, especially when dealing with limited labeled data. This survey explores the mitigation of the overfitting issue through a comprehensive examination of...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10699340/ |
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| _version_ | 1850064907601444864 |
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| author | Teerath Kumar Rob Brennan Alessandra Mileo Malika Bendechache |
| author_facet | Teerath Kumar Rob Brennan Alessandra Mileo Malika Bendechache |
| author_sort | Teerath Kumar |
| collection | DOAJ |
| description | Deep learning algorithms have exhibited impressive performance across various computer vision tasks; however, the challenge of overfitting persists, especially when dealing with limited labeled data. This survey explores the mitigation of the overfitting issue through a comprehensive examination of image data augmentation techniques, which aim to enhance dataset size and diversity by introducing varied samples. The survey exclusively focuses on these techniques, presenting an insightful overview and introducing a novel taxonomy. The discussion encompasses the strengths and limitations of these techniques. Additionally, the paper provides extensive results evaluating the impact of these techniques on prevalent computer vision tasks: image classification, object detection, and semantic segmentation. The survey concludes with an examination of challenges, limitations, and potential future research directions. |
| format | Article |
| id | doaj-art-90e97573b86148eab3acab3df031ce89 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-90e97573b86148eab3acab3df031ce892025-08-20T02:49:09ZengIEEEIEEE Access2169-35362024-01-011218753618757110.1109/ACCESS.2024.347012210699340Image Data Augmentation Approaches: A Comprehensive Survey and Future DirectionsTeerath Kumar0https://orcid.org/0000-0001-8769-4989Rob Brennan1https://orcid.org/0000-0001-8236-362XAlessandra Mileo2https://orcid.org/0000-0002-6614-6462Malika Bendechache3https://orcid.org/0000-0003-0069-1860CRT-AI and ADAPT Research Centre, School of Computing, Dublin City University, Dublin 9, IrelandADAPT Research Centre, School of Computer Science, University College Dublin, Dublin 4, IrelandINSIGHT and I-Form Research Centre, School of Computing, Dublin City University, Dublin 9, IrelandADAPT and Lero Research Centre, School of Computer Science, University of Galway, Galway, IrelandDeep learning algorithms have exhibited impressive performance across various computer vision tasks; however, the challenge of overfitting persists, especially when dealing with limited labeled data. This survey explores the mitigation of the overfitting issue through a comprehensive examination of image data augmentation techniques, which aim to enhance dataset size and diversity by introducing varied samples. The survey exclusively focuses on these techniques, presenting an insightful overview and introducing a novel taxonomy. The discussion encompasses the strengths and limitations of these techniques. Additionally, the paper provides extensive results evaluating the impact of these techniques on prevalent computer vision tasks: image classification, object detection, and semantic segmentation. The survey concludes with an examination of challenges, limitations, and potential future research directions.https://ieeexplore.ieee.org/document/10699340/Computer visiondata augmentationdeep learningimage classificationobject detectionsegmentation |
| spellingShingle | Teerath Kumar Rob Brennan Alessandra Mileo Malika Bendechache Image Data Augmentation Approaches: A Comprehensive Survey and Future Directions IEEE Access Computer vision data augmentation deep learning image classification object detection segmentation |
| title | Image Data Augmentation Approaches: A Comprehensive Survey and Future Directions |
| title_full | Image Data Augmentation Approaches: A Comprehensive Survey and Future Directions |
| title_fullStr | Image Data Augmentation Approaches: A Comprehensive Survey and Future Directions |
| title_full_unstemmed | Image Data Augmentation Approaches: A Comprehensive Survey and Future Directions |
| title_short | Image Data Augmentation Approaches: A Comprehensive Survey and Future Directions |
| title_sort | image data augmentation approaches a comprehensive survey and future directions |
| topic | Computer vision data augmentation deep learning image classification object detection segmentation |
| url | https://ieeexplore.ieee.org/document/10699340/ |
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