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Automated fish cage net inspection using image processing techniques

Paspalakis Stavros, Moirogiorgou Konstantia, Papandroulakis Nikos, Giakos George, Zervakis Michail

Πλήρης Εγγραφή


URI: http://purl.tuc.gr/dl/dias/09E7AFA1-5168-46F6-A5D5-D121385FE7E9
Έτος 2020
Τύπος Δημοσίευση σε Περιοδικό με Κριτές
Άδεια Χρήσης
Λεπτομέρειες
Βιβλιογραφική Αναφορά S. Paspalakis, K. Moirogiorgou, N. Papandroulakis, G. Giakos, and M. Zervakis, “Automated fish cage net inspection using image processing techniques,” IET Image Process., vol. 14, no. 10, pp. 2028–2034, Aug. 2020. doi: 10.1049/iet-ipr.2019.1667 https://doi.org/10.1049/iet-ipr.2019.1667
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Περίληψη

Fish-cage dysfunction in aquaculture installations can trigger significant negative consequences affecting the operational costs. Low oxygen levels, due to excessive fooling's, leads to decrease growth performance, and feed efficiency. Therefore, frequent periodic inspection of fish-cage nets is required, but this task can become quite expensive with the traditional means of employing professional divers that perform visual inspections at regular time intervals. The modern trend in aquaculture is to take advantage of IT technologies with the use of a small-sized, low-cost autonomous underwater vehicle, permanently residing within a fish cage and performing regular video inspection of the infrastructure for the entire net surface. In this study, we explore specialised image processing schemes to detect net holes of multiple area size and shape. These techniques are designed with the vision to provide robust solutions that take advantage of either global or local image structures to provide the efficient inspection of multiple net holes.

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