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.

中文翻译：

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使用图像处理技术自动检查鱼笼网

水产养殖设施中的鱼笼功能障碍会引发严重的负面影响，从而影响运营成本。由于过度愚弄而导致的低氧含量会导致生长性能和饲料效率下降。因此，需要对鱼笼网进行频繁的定期检查，但是采用雇用专业潜水员的常规方法，该任务会变得非常昂贵，该专业潜水员应按固定的时间间隔进行目视检查。水产养殖的现代趋势是利用IT技术，使用小型，低成本的自动水下设备，将其永久性地安置在鱼笼中，并对整个网的基础设施进行定期的视频检查。在这项研究中，我们探索专门的图像处理方案来检测多个区域大小和形状的网孔。