Automated fish cage net inspection using image processing techniques

Stavros Paspalakis; Konstantia Moirogiorgou; Nikos Papandroulakis; George Giakos; Michalis Zervakis

Automated fish cage net inspection using image processing techniques

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Author(s): Stavros Paspalakis¹ ; Konstantia Moirogiorgou¹ ; Nikos Papandroulakis² ; George Giakos³ ; Michalis Zervakis¹
- Affiliations: 1: School of Electrical & Computer Engineering, Technical University of Crete, University Campus , 73100 Chania, Crete , Greece ;
  2: Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research , Thalassocosmos, P.C. 71500, Gournes Heraklion, Crete , Greece ;
  3: Department of Electrical and Computer Engineering , Science and Technology Integration Research Laboratory, Manhattan College , Riverdale, NY , USA
Source: Volume 14, Issue 10, 21 August 2020, p. 2028 – 2034
DOI: 10.1049/iet-ipr.2019.1667 , Print ISSN 1751-9659, Online ISSN 1751-9667

Received 06/12/2019, Accepted 01/06/2020, Revised 18/04/2020, Published 11/06/2020

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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Automated fish cage net inspection using image processing techniques

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