期刊
IET IMAGE PROCESSING
卷 14, 期 10, 页码 2028-2034出版社
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-ipr.2019.1667
关键词
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
类别
资金
- EU H2020 research and innovation programme [678396]
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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