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.netYuriy 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 classificationReferences
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Authors
Rizki MultajamUniversiti Malaysia Terengganu Malaysia
Authors
Ahmad Faisal Mohamad AyobUniversiti Malaysia Terengganu Malaysia
Authors
W.S. Mada SanjayaUniversitas Islam Negeri Sunan Gunung Djati Indonesia
Authors
Aceng SambasUniversiti Sultan Zainal Abidin Malaysia
Authors
Volodymyr Rusynrusyn_v@ukr.net
Yuriy Fedkovych Chernivtsi National University, Department of Radio Engineering and Information Ukraine
https://orcid.org/0000-0001-6219-1031
Authors
Andrii SamilaYuriy Fedkovych Chernivtsi National University Ukraine
Statistics
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