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Underwater Human and Fish Detection
Subject area: Science,Engineering and Technology · Area of research: Computer Science And Engineering
Abstract
This system aims to create an advanced underwater detection and classification solution utilizing a waterproof USB camera. It is designed to detect humans (both alive and deceased) and classify fish species and gender in real-time. The hardware setup includes a high-resolution waterproof USB camera with infrared (IR) functionality, allowing for effective operation in low-light underwater environments. For the software, sophisticated image processing techniques are employed, utilizing OpenCV and the YOLO (You Only Look Once) algorithm for efficient human and object detection. Human detection is carried out by analyzing posture and movement, distinguishing between living individuals and deceased bodies. To classify fish, convolutional neural networks (CNNs) are trained on labeled datasets, such as Fish4Knowledge, to identify species and determine gender. A user-friendly dashboard displays real-time data, providing visualizations, alerts, and facilitating easy monitoring. This solution has promising applications in areas like underwater exploration, marine life monitoring, and search-and-rescue operations, offering an automated and effective means of detecting and classifying underwater objects.
References
[1] Underwater Acoustic Signal, March 1,2024
[2] Fast Accurate Fish Detection and Underwater Images by Neural Network 2023
[3] Underwater Human Body Detection Using Faster CNN and RNN
[4] https://github.com/pathikg/Underwater-fish-detection
[5] https://youtu.be/N9JsJGvw45s?si=26wkROsXSHJQuDEh
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How to cite this paper
@article{1707862,
author = {Suhas N, A Kushanth Reddy, Paneendra L, Dr. Jagadeesha R},
title = {Underwater Human and Fish Detection},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {10},
pages = {403-407},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1707862.pdf},
abstract = {This system aims to create an advanced underwater detection and classification solution utilizing a waterproof USB camera. It is designed to detect humans (both alive and deceased) and classify fish species and gender in real-time. The hardware setup includes a high-resolution waterproof USB camera with infrared (IR) functionality, allowing for effective operation in low-light underwater environments. For the software, sophisticated image processing techniques are employed, utilizing OpenCV and the YOLO (You Only Look Once) algorithm for efficient human and object detection. Human detection is carried out by analyzing posture and movement, distinguishing between living individuals and deceased bodies. To classify fish, convolutional neural networks (CNNs) are trained on labeled datasets, such as Fish4Knowledge, to identify species and determine gender. A user-friendly dashboard displays real-time data, providing visualizations, alerts, and facilitating easy monitoring. This solution has promising applications in areas like underwater exploration, marine life monitoring, and search-and-rescue operations, offering an automated and effective means of detecting and classifying underwater objects.},
month = {April},
}