| Autor: |
Barbosa Bessa, Willian Ramon, Mendes Neto, Francisco Milton, Nunes Barbosa, Vinícius, Gualberto Leite, Danielly, Crispin Braga, Oton, Wedney de Lima, Mário, Souza dos Santos, Vinícius |
| Předmět: |
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| Zdroj: |
CISTI (Iberian Conference on Information Systems & Technologies / Conferência Ibérica de Sistemas e Tecnologias de Informação) Proceedings; 2023, Issue 18, p1-11, 11p |
| Abstrakt: |
Aquaculture is the process of cultivating organisms with a predominantly aquatic habitat, being today an important activity in human food production. Despite its importance, there are still several activities that are carried out almost exclusively manually. Among them, it is possible to highlight the counting of animals. Therefore, this study presents a set of solutions to support the activity of counting aquatic animals using computer vision and machine learning techniques, through deep learning, with the differential that the end user will be able to access the solutions via a smartphone. Currently, the model is 99% accurate. A counting model based on YOLOv4 was also developed, which reached 98.50% of mAP and 98.70% of accuracy, thus obtaining an excellent result. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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