Estimation of Artificial Reef Pose Based on Deep Learning Article Swipe
Yifan Song
,
Zuli Wu
,
Shengmao Zhang
,
Weimin Quan
,
Yongchuang Shi
,
Xinquan Xiong
,
Penglong Li
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/jmse12050812
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/jmse12050812
Artificial reefs are man-made structures submerged in the ocean, and the design of these structures plays a crucial role in determining their effectiveness. Precisely measuring the configuration of artificial reefs is vital for creating suitable habitats for marine organisms. This study presents a novel approach for automated detection of artificial reefs by recognizing their key features and key points. Two enhanced models, namely, YOLOv8n-PoseRFSA and YOLOv8n-PoseMSA, are introduced based on the YOLOv8n-Pose architecture. The YOLOv8n-PoseRFSA model exhibits a 2.3% increase in accuracy in pinpointing target key points compared to the baseline YOLOv8n-Pose model, showcasing notable enhancements in recall rate, mean average precision (mAP), and other evaluation metrics. In response to the demand for swift identification in mobile fishing scenarios, a YOLOv8n-PoseMSA model is proposed, leveraging MobileNetV3 to replace the backbone network structure. This model reduces the computational burden to 33% of the original model while preserving recognition accuracy and minimizing the accuracy drop. The methodology outlined in this research enables real-time monitoring of artificial reef deployments, allowing for the precise quantification of their structural characteristics, thereby significantly enhancing monitoring efficiency and convenience. By better assessing the layout of artificial reefs and their ecological impact, this approach offers valuable data support for the future planning and implementation of reef projects.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/jmse12050812
- https://www.mdpi.com/2077-1312/12/5/812/pdf?version=1715607700
- OA Status
- gold
- Cited By
- 6
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396929874
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4396929874Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/jmse12050812Digital Object Identifier
- Title
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Estimation of Artificial Reef Pose Based on Deep LearningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-13Full publication date if available
- Authors
-
Yifan Song, Zuli Wu, Shengmao Zhang, Weimin Quan, Yongchuang Shi, Xinquan Xiong, Penglong LiList of authors in order
- Landing page
-
https://doi.org/10.3390/jmse12050812Publisher landing page
- PDF URL
-
https://www.mdpi.com/2077-1312/12/5/812/pdf?version=1715607700Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2077-1312/12/5/812/pdf?version=1715607700Direct OA link when available
- Concepts
-
Reef, Artificial reef, Computer science, Artificial intelligence, Key (lock), Baseline (sea), Machine learning, Data mining, Ecology, Fishery, Computer security, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 1Per-year citation counts (last 5 years)
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31Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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