Real-time detection and classification of fish in underwater environment using YOLOv5: a comparative study of deep learning architectures

• Autorzy: Multajam Rizki , Ayob Ahmad Faisal Mohamad , Sanjaya W.S. Mada , Sambas Aceng , Rusyn Volodymyr , Samila Andrii

Identyfikatory

DOI

10.35784/iapgos.6022

Warianty tytułu

PL

Wykrywanie i klasyfikacja ryb w czasie rzeczywistym w środowisku podwodnym przy użyciu YOLOv5: badanie porównawcze architekturgłębokiego uczenia

• Języki publikacji: EN

Abstrakty

EN

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.

PL

Niniejszy artykuł bada metody wykrywania i klasyfikacji ryb jako integralną część podwodnych systemów monitorowania środowiska. Wykorzystując innowacyjne podejście, badania koncentrują się na opracowaniu metod w czasie rzeczywistym do bardzo dokładnego wykrywania i klasyfikacji ryb. Wprowadzenie zaawansowanych technologii, takich jak YOLO (You Only Look Once) V5, stanowi podstawę wydajnego i responsywnego systemu. Badanie ocenia również różne podejścia w kontekście głębokiego uczenia się, aby porównać wydajność i dokładność wykrywania i klasyfikacji ryb. Oczekuje się, że wyniki tych badań przyczynią się do rozwoju bardziej zaawansowanych i wydajnych systemów monitorowania zbiorników wodnych w celu zrozumienia podwodnych ekosystemów i wysiłków na rzecz ochrony przyrody.

Słowa kluczowe

EN

deep learning; YOLOv5; real-time methods; ONNX; automatic fish detection and classification

PL

deep learning; YOLOv5; metody czasu rzeczywistego; ONNX; automatyczne wykrywanie i klasyfikacja ryb

• Wydawca: Wydawnictwo Politechniki Lubelskiej
• Czasopismo: Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
• Rocznik: 2024
• Tom: T. 14, nr 3
• Strony: 91--95
• Opis fizyczny: Bibliogr. 29 poz., rys., wykr.

Twórcy

autor

Multajam Rizki

• Universiti Malaysia Terengganu, Faculty of Ocean Engineering Technology and Informatics, Kuala Nerus, Malaysia

• https://orcid.org/0009-0005-3657-4588

autor

Ayob Ahmad Faisal Mohamad

• Universiti Malaysia Terengganu, Faculty of Ocean Engineering Technology and Informatics, Kuala Nerus, Malaysia

• https://orcid.org/0000-0001-7123-4662

autor

Sanjaya W.S. Mada

• Universitas Islam Negeri Sunan Gunung Djati, Department of Physics, Bandung, Indonesia

• https://orcid.org/0000-0003-1963-193X

autor

Sambas Aceng

• Universiti Sultan Zainal Abidin, Faculty of Informatics and Computing, Campus Besut, Malaysia
• Universitas Muhammadiyah Tasikmalaya, Department of Mechanical Engineering, Tasikmalaya, Indonesia

• https://orcid.org/0000-0002-1623-0770

autor

Rusyn Volodymyr

• Yuriy Fedkovych Chernivtsi National University, Department of Radio Engineering and Information Security, Chernivtsi, Ukraine

• https://orcid.org/0000-0001-6219-1031

autor

Samila Andrii

• Yuriy Fedkovych Chernivtsi National University, Department of Radio Engineering and Information Security, Chernivtsi, Ukraine

• https://orcid.org/0000-0001-8279-9116+

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