An integrated ROV solution for underwater net-cage inspection in fish farms using computer vision

This paper reports the integration of a remotely operated vehicle (ROV) solution for monitoring water quality in fish farms. The robotic system includes a RGB camera for real-time video capturing and a set of integrated sensors to measure hydro-climatic data. Computer vision algorithms were implemented with the aim of inspecting net-cages in fish farms. A comprehensive software solution was developed to allow a seamless use of the vision algorithms proposed in this work. Our system was designed to process underwater imagery captured by the ROV in order to determine net patterns associated with net failure. The system was tested in a dam under real conditions. ROC data were computed to demonstrate the accuracy of the proposed system during underwater fish cage inspection. On average, we obtained an accuracy of 0.91 regarding net pattern reconstruction tasks, while an accuracy of 0.79 for net damage detection under different underwater scenarios.

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