Data capture and real-time data quality analysis

Abstract

This report presents results obtained in the CageReporter project regarding the development of a 3D vision system to be used for data capture in fish cages. The developed system enables to obtain high-quality data with the overall goal to identify fish conditions and perform cage inspections during daily operations, as well as the robotic vision for an underwater vehicle during the adaptive operation planning in the cage. A compact and robust sensor with optical components and lighting system was developed. In addition, this activity presents development of methods to evaluate the quality of the captured data. Based on defined quality criteria associated with fish conditions and cage inspection operations, algorithms have been developed to evaluate whether the quality criteria are met. The algorithms have been validated using image data obtained from 24/7 video streams from a full-scale fish cage. The work furthermore includes the development of image processing algorithms to estimate the distance and orientation relative to the inspected object of interest, such as the fish or the net. The developed algorithms have been validated based on vision data obtained during tests both in lab- and full scale.