REAL-TIME DETECTION AND CLASSIFICATION OF FISH IN UNDERWATER ENVIRONMENT USING YOLOV5: A COMPARATIVE STUDY OF DEEP LEARNING ARCHITECTURES

Rizki Multajam


Universiti Malaysia Terengganu (Malaysia)

Ahmad Faisal Mohamad Ayob


Universiti Malaysia Terengganu (Malaysia)

W.S. Mada Sanjaya


Universitas Islam Negeri Sunan Gunung Djati (Indonesia)

Aceng Sambas


Universiti Sultan Zainal Abidin (Malaysia)

Volodymyr Rusyn

rusyn_v@ukr.net
Yuriy Fedkovych Chernivtsi National University, Department of Radio Engineering and Information (Ukraine)
https://orcid.org/0000-0001-6219-1031

Andrii Samila


Yuriy Fedkovych Chernivtsi National University (Ukraine)

Abstract

This article explores techniques for the detection and classification of fish as an integral part of underwater environmental monitoring systems. Employing an innovative approach, the study focuses on developing real-time methods for high-precision fish detection and classification. The implementation of cutting-edge technologies, such as YOLO (You Only Look Once) V5, forms the basis for an efficient and responsive system. The study also evaluates various approaches in the context of deep learning to compare the performance and accuracy of fish detection and classification. The results of this research are expected to contribute to the development of more advanced and effective aquatic monitoring systems for understanding underwater ecosystems and conservation efforts.


Keywords:

Deep learning, YOLOv5, real-time methods, ONNX, automatic fish detection and classification

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Published
2024-09-30

Cited by

Multajam, R., Mohamad Ayob, A. F., Sanjaya, W. M., Sambas, A., Rusyn, V., & Samila, A. (2024). REAL-TIME DETECTION AND CLASSIFICATION OF FISH IN UNDERWATER ENVIRONMENT USING YOLOV5: A COMPARATIVE STUDY OF DEEP LEARNING ARCHITECTURES. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 14(3), 91–95. https://doi.org/10.35784/iapgos.6022

Authors

Rizki Multajam 

Universiti Malaysia Terengganu Malaysia

Authors

Ahmad Faisal Mohamad Ayob 

Universiti Malaysia Terengganu Malaysia

Authors

W.S. Mada Sanjaya 

Universitas Islam Negeri Sunan Gunung Djati Indonesia

Authors

Aceng Sambas 

Universiti Sultan Zainal Abidin Malaysia

Authors

Volodymyr Rusyn 
rusyn_v@ukr.net
Yuriy Fedkovych Chernivtsi National University, Department of Radio Engineering and Information Ukraine
https://orcid.org/0000-0001-6219-1031

Authors

Andrii Samila 

Yuriy Fedkovych Chernivtsi National University Ukraine

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