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In this work, acoustic and computer vision techniques are combined to develop an automatic procedure for biomass estimation of tuna during transfers. A side scan sonar working at 200 kHz and a stereo camera, positioned facing towards the surface to record the ventral aspect of fish, are set as acquisition equipment. Moreover, a floating structure has been devised to place the sensors between cages in transfers, creating a transfer canal that allows data acquisition while fish swim from donor to receiving cage. Biomass assessment is computed by counting transferred tuna and sizing a representative sample of the stock. The number of transferred tuna is automatically deduced from acoustic echograms by means of image processing techniques, whereas tuna size is computed from the stereo videos using our automatic computer vision procedure based on a deformable model of the fish ventral silhouette. The results show that the system achieves automatic tuna counting with error below 10%, achieving around 1% error in the best configuration, and automatic tuna sizing of more than 20% of the stock, with highly accurate Snout Fork Length estimation when compared to true data from harvests. These results fulfil the requirements imposed by International Commission for the Conservation of Atlantic Tunas for compliant transfer operations. [ABSTRACT FROM AUTHOR]

A proposal is described for an underwater sensor combining an acoustic device with an optical one to automatically size juvenile bluefin tuna from a ventral perspective. Acoustic and optical information is acquired when the tuna are swimming freely and the fish cross our combined sensor’s field of view. Image processing techniques are used to identify and classify fish traces in acoustic data (echogram), while the video frames are processed by fitting a deformable model of the fishes’ ventral silhouette. Finally, the fish are sized combining the processed acoustic and optical data, once the correspondence between the two kinds of data is verified. The proposed system is able to automatically give accurate measurements of the tuna’s Snout-Fork Length (SFL) and width. In comparison with our previously validated automatic sizing procedure with stereoscopic vision, this proposal improves the samples per hour of computing time by 7.2 times in a tank with 77 juveniles of Atlantic bluefin tuna (Thunnus thynnus), without compromising the accuracy of the measurements. This work validates the procedure for combining acoustic and optical data for fish sizing and is the first step towards an embedded sensor, whose electronics and processing capabilities should be optimized to be autonomous in terms of the power supply and to enable real-time processing.

• Estimation of Bluefin Tuna mean length in sea cages has been performed using underwater acoustic tools. • The combined use of optical and acoustical techniques has revealed a strong relationship between TS and ABFT length measured in fattening cages. • Good agreement was found between TS and the fish fork (without the need to know the tuna's tilt angle), even considering uncompensated TS variable. This paper proposes an indirect method to estimate Bluefin Tuna (Thunnus thynnus) biomass in cages using acoustic techniques. Two Simrad EK60 echosounders working at 120 and 200 kHz and a stereo camera were used to obtain target strength (TS) to fork length (FL) relationships for both operating frequencies. The equipment was placed at the bottom of a floating cage, facing towards the surface to record the ventral aspect of fish. The acoustic and optical recordings were automatically analysed and the combination of acoustic and optical results provided unequivocal TS-FL assignments. Good relationships between TS and FL were obtained for both frequencies even without discriminating data from different fish tilts and without using beam directivity compensation. Stronger correlations were obtained for compensated TS at 200 kHz when reduced tuna swimming tilt was considered. TS measurements were compared to MFS numerical predictions for a Bluefin swimbladder model, with the simulation results showing good agreement with experimental measurements. The results allow the mean tuna length to be predicted in growing or fattening cages from acoustic data raising the possibility of improving production management and of providing a useful tool for catch control estimations made by international organisations (like ICCAT) and government bodies. [ABSTRACT FROM AUTHOR]

A proposal is described for an underwater sensor combining an acoustic device with an optical one to automatically size juvenile bluefin tuna from a ventral perspective. Acoustic and optical information is acquired when the tuna are swimming freely and the fish cross our combined sensor's field of view. Image processing techniques are used to identify and classify fish traces in acoustic data (echogram), while the video frames are processed by fitting a deformable model of the fishes' ventral silhouette. Finally, the fish are sized combining the processed acoustic and optical data, once the correspondence between the two kinds of data is verified. The proposed system is able to automatically give accurate measurements of the tuna's Snout-Fork Length (SFL) and width. In comparison with our previously validated automatic sizing procedure with stereoscopic vision, this proposal improves the samples per hour of computing time by 7.2 times in a tank with 77 juveniles of Atlantic bluefin tuna (Thunnus thynnus), without compromising the accuracy of the measurements. This work validates the procedure for combining acoustic and optical data for fish sizing and is the first step towards an embedded sensor, whose electronics and processing capabilities should be optimized to be autonomous in terms of the power supply and to enable real-time processing. [ABSTRACT FROM AUTHOR]