Deep Learning in Computer Vision: Principles and Applications
Abstract
Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained u ...
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Deep learning-based object detection methods have been rapidly improved and advanced.
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From A deep learning model for steel surface defect detection (2024) · cites this paper as [8]
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MethodCyclicNet: an alternately updated network for semantic segmentation
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“It has been widely applied in medical image diagnosis [6, 22, 40], pedestrian detection [23, 24, 37], video object segmentation [8, 20, 34] and other fields [3, 5, 10, 28], which has great research significance and value.”
BackgroundClassification of industrial surface defects based on neural architecture search
Yang, T., Zhang, T., & Huang, L. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09968-2 · Cited by 7 · see citing works
“Convolutional neural networks (CNNs) can automatically extract effective target features from a large quantity of labeled data and perform image classification tasks well [8, 10, 11, 29, 30]; accordingly, they can also be applied to SDC tasks [14].”
Background
- Authors
- Hassaballah, M., & Awad, A. I.
- Venue
- CRC Press eBooks
- Publisher
- CRC Press
- Published
- 2020-03-23
- OpenAlex
- W3013682536
- Cited by
- 192
- References
- 0
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 29 · 2023: 41 · 2022: 43 · 2021: 42 · 2020: 18
- Type
- reference entry
- Language
- en
- Open access
- closed
- Locations
- CRC Press eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- deep learning, computer vision
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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