A Feature-Enhanced and Adaptive Routing Framework for Fish School Detection on AUVs for Degraded Underwater Imaging Environments-Reference-Cited by-同舟云学术

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A Feature-Enhanced and Adaptive Routing Framework for Fish School Detection on AUVs for Degraded Underwater Imaging Environments

Published:2024-05-15 Issue:10 Volume:11 Page:18335-18350
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Jiang Yu¹^ORCID,Wang Yuehang¹^ORCID,Zhang Yongji¹^ORCID,Guo Qianren²^ORCID,Zhao Minghao³^ORCID,Qin Hongde⁴^ORCID

Affiliation:

1. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education and the College of Computer Science and Technology, Jilin University, Changchun, China

2. College of Software, Jilin University, Changchun, China

3. College of Earth Sciences, Jilin University, Changchun, China

4. Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488907/10528217/10421781.pdf?arnumber=10421781

Reference69 articles.

1. The potential of video imagery from worldwide cabled observatory networks to provide information supporting fish-stock and biodiversity assessment

2. Mesoscale assessment of sedentary coastal fish density using vertical underwater cameras

3. Deep learning

4. Deep learning for visual understanding: A review

5. You Only Look Once: Unified, Real-Time Object Detection

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