Automated fish cage net inspection using image processing techniques. Issue 10 (6th July 2020)

• Record Type: Journal Article
• Authors: Paspalakis, Stavros; Moirogiorgou, Konstantia; Papandroulakis, Nikos; Giakos, George; Zervakis, Michalis
• Is Part Of: IET image processing. Volume 14:Issue 10(2020)
• Page Start: 2028
• Page End: 2034
• Publication Date: 2020-07-06
• Subjects: aquaculture -- automatic optical inspection -- object detection -- video signal processing -- robot vision -- autonomous underwater vehicles -- mobile robots; automated fish cage net inspection -- image processing -- fish‐cage dysfunction -- aquaculture installations -- visual inspections -- autonomous underwater vehicle -- video inspection -- global image structures -- local image structures -- IT technologies -- net holes detection; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.1667
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.