Pregled bibliografske jedinice broj: 1151248
Collecting information for biomass estimation in mariculture with a heterogeneous robotic system
Collecting information for biomass estimation in mariculture with a heterogeneous robotic system // Proceedings of the MIPRO 2021 - 44th International Convention
Opatija, 2021. str. 1295-1300 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1151248 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Collecting information for biomass estimation in
mariculture with a heterogeneous robotic system
Autori
Rezo, Marko ; Čagalj, Kristijan-Matan ; Kovačić, Zdenko
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the MIPRO 2021 - 44th International Convention
/ - Opatija, 2021, 1295-1300
Skup
44th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2021)
Mjesto i datum
Opatija, Hrvatska, 27.09.2021. - 01.10.2021
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
marine fish farming ; heterogeneous robotics systems ; fish counting ; fish biomass estimation
Sažetak
In this paper, we address the problem of fish stock estimation in marine fisheries using a heterogeneous robotic system consisting of unmanned aerial vehicles (UAVs) and unmanned underwater vehicles (UUVs). UAVs take aerial photographs of the cage during fish feeding, while UUVs take photographs of fish from top to bottom in the cage. The photos and videos obtained provide the input data for estimating the number of fish and the amount of biomass of fish in the cage. The paper analyzes a number of factors that affect the accuracy of the estimate. Preliminary results obtained with an approximate method for estimating the number of fish, based on the processing of images obtained in a virtual simulator and resembling aerial photographs of fish taken during feeding, are described. The results obtained show that this problem is extremely complex and that it is worth trying to use machine learning and artificial intelligence methods to achieve the desired maximum estimation error of less than 20%.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
Profili:
Zdenko Kovačić
(autor)