학술논문

Development of an Underwater Environment Analysis System Based on Neural Network Using Omnidirectional Camera
Document Type
Conference
Source
2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems (SCIS&ISIS) Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems (SCIS&ISIS), 2024 Joint 13th International Conference on. :1-3 Nov, 2024
Subject
Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Atmospheric measurements
Neural networks
Estimation
Turbidity
Fish
Cameras
Particle measurements
Fish detection
Turbidity estimation
Neural Network
Deep Learning
Language
Abstract
The 30by30 goal was pledged at the G7 summit held in 2021. This goal aims to conserve more than 30 % of both terrestrial and marine areas as natural environment areas by 2030. In particular, observing and analyzing underwater environments is essential for preserving natural environment areas in marine areas. However, investigating underwater environments poses significant risks and requires substantial human resources. Consequently, there is a demand for systems that can measure and automatically analyze underwater conditions. This paper proposes the development of an underwater measurement system utilizing a waterproofed omnidirectional camera, along with a system for analyzing the collected data. Specifically, the system employs neural network-based techniques for fish detection and turbidity estimation.