An Automated Framework Based on Deep Learning for Shark Recognition Article Swipe
Nhat Anh Le
,
Jucheol Moon
,
Christopher G. Lowe
,
Hyunil Kim
,
Sang‐Il Choi
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/jmse10070942
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/jmse10070942
The recent progress in deep learning has given rise to a non-invasive and effective approach for animal biometrics. These modern techniques allow researchers to track animal individuals on a large-scale image database. Typical approaches are suited to a closed-set recognition problem, which is to identify images of known objects only. However, such approaches are not scalable because they mis-classify images of unknown objects. To recognize the images of unknown objects as ‘unknown’, a framework should be able to deal with the open set recognition scenario. This paper proposes a fully automatic, vision-based identification framework capable of recognizing shark individuals including those that are unknown. The framework first detects and extracts the shark from the original image. After that, we develop a deep network to transform the extracted image to an embedding vector in latent space. The proposed network consists of the Visual Geometry Group-UNet (VGG-UNet) and a modified Visual Geometry Group-16 (VGG-16) network. The VGG-UNet is utilized to detect shark bodies, and the modified VGG-16 is used to learn embeddings of shark individuals. For the recognition task, our framework learns a decision boundary using a one-class support vector machine (OSVM) for each shark included in the training phase using a few embedding vectors belonging to them, then it determines whether a new shark image is recognized as belonging to a known shark individual. Our proposed network can recognize shark individuals with high accuracy and can effectively deal with the open set recognition problem with shark images.
Related Topics
Concepts
Artificial intelligence
Computer science
Embedding
Identification (biology)
Set (abstract data type)
Pattern recognition (psychology)
Deep learning
Image (mathematics)
Support vector machine
Task (project management)
Scale (ratio)
Computer vision
Machine learning
Geography
Cartography
Biology
Programming language
Management
Botany
Economics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/jmse10070942
- https://www.mdpi.com/2077-1312/10/7/942/pdf?version=1657519489
- OA Status
- gold
- Cited By
- 8
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4285014064
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4285014064Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/jmse10070942Digital Object Identifier
- Title
-
An Automated Framework Based on Deep Learning for Shark RecognitionWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-07-09Full publication date if available
- Authors
-
Nhat Anh Le, Jucheol Moon, Christopher G. Lowe, Hyunil Kim, Sang‐Il ChoiList of authors in order
- Landing page
-
https://doi.org/10.3390/jmse10070942Publisher landing page
- PDF URL
-
https://www.mdpi.com/2077-1312/10/7/942/pdf?version=1657519489Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2077-1312/10/7/942/pdf?version=1657519489Direct OA link when available
- Concepts
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Artificial intelligence, Computer science, Embedding, Identification (biology), Set (abstract data type), Pattern recognition (psychology), Deep learning, Image (mathematics), Support vector machine, Task (project management), Scale (ratio), Computer vision, Machine learning, Geography, Cartography, Biology, Programming language, Management, Botany, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 3, 2023: 2, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
20Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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