WYKRYWANIE I KLASYFIKACJA RYB W CZASIE RZECZYWISTYM W ŚRODOWISKU PODWODNYM PRZY UŻYCIU YOLOV5: BADANIE PORÓWNAWCZE ARCHITEKTUR GŁĘBOKIEGO UCZENIA

Rizki Multajam


Universiti Malaysia Terengganu (Malezja)

Ahmad Faisal Mohamad Ayob


Universiti Malaysia Terengganu (Malezja)

W.S. Mada Sanjaya


Universitas Islam Negeri Sunan Gunung Djati (Indonezja)

Aceng Sambas


Universiti Sultan Zainal Abidin (Malezja)

Volodymyr Rusyn

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

Andrii Samila


Yuriy Fedkovych Chernivtsi National University (Ukraina)

Abstrakt

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:

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

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Opublikowane
2024-09-30

Cited By / Share

Multajam, R., Mohamad Ayob, A. F., Sanjaya, W. M., Sambas, A., Rusyn, V., & Samila, A. (2024). WYKRYWANIE I KLASYFIKACJA RYB W CZASIE RZECZYWISTYM W ŚRODOWISKU PODWODNYM PRZY UŻYCIU YOLOV5: BADANIE PORÓWNAWCZE ARCHITEKTUR GŁĘBOKIEGO UCZENIA. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 14(3), 91–95. https://doi.org/10.35784/iapgos.6022

Autorzy

Rizki Multajam 

Universiti Malaysia Terengganu Malezja

Autorzy

Ahmad Faisal Mohamad Ayob 

Universiti Malaysia Terengganu Malezja

Autorzy

W.S. Mada Sanjaya 

Universitas Islam Negeri Sunan Gunung Djati Indonezja

Autorzy

Aceng Sambas 

Universiti Sultan Zainal Abidin Malezja

Autorzy

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

Autorzy

Andrii Samila 

Yuriy Fedkovych Chernivtsi National University Ukraina

Statystyki

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