Pattern Recognition Algorithm To Identify Marine Animals Using Ai/Ml

DOI

10.2991/978-94-6463-738-0_62How to use a DOI?

Keywords

Coral Reef Ecosystems; Marine Animal Detection; Deep Learning Techniques; YOLO-based Models; Underwater Environmental Monitoring

Abstract

The ecological significance of reef-building coral reef ecosystems in warm shallow waters has been brought to light in recent years by developments in environmental research and marine resources. For the protection of ecosystems, monitoring marine animal populations is essential. Real-time environmental awareness is also necessary for autonomous underwater vehicles (AUVs) to make better decisions. As a result, finding marine animals has grown to be a major research problem.

Although there are still issues, deep neural network models have demonstrated promise in tasks involving fish. Several YOLO-based techniques for ideTntifying marine animals on coral reefs are compared in this research. The outcomes of the experiments show that these techniques are capable of properly and swiftly identifying aquatic life. Additionally, suggestions for model enhancement based on evaluation findings are provided. Deep learning methods have helped marine ecology by enabling real-time analysis of complicated data from cameras, acoustic sensors, and corals.

The richness of underwater backgrounds, image quality, and the variety of animal behaviors make it difficult to detect marine life. Certain marine species with bounding boxes in photos have been identified using deep learning-based methods. Object identification, categorization, tracking, and data segmentation pertaining to plankton, fish, marine mammals, and nutrient cycling are all included in case studies. Notwithstanding obstacles, deep learning methods exhibit encouraging outcomes and have the potential to safeguard maritime environments.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Toshaniwali Bhargav
AU  - Somprakash Nayak
AU  - Chetan Manik
AU  - Khusnandan Patels
PY  - 2025
DA  - 2025/06/22
TI  - Pattern Recognition Algorithm To Identify Marine Animals Using Ai/Ml
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 781
EP  - 790
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_62
DO  - 10.2991/978-94-6463-738-0_62
ID  - Bhargav2025
ER  -