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This paper proposes a robust deformable adaptive 2D model, based on computer vision methods, that automatically fits the body (ventral silhouette) of Bluefin tuna while swimming. Our model (without human intervention) adjusts to fish shape and size, obtaining fish orientation, bending to fit their flexion motion and has proved robust enough to overcome possible segmentation inaccuracies. Once the model has been successfully fitted to the fish it can ensure that the detected object is a tuna and not parts of fish or other objects. Automatic requirements of the fishing industry like biometric measurement, specimen counting or catch biomass estimation could then be addressed using a stereoscopic system and meaningful information extracted from our model. We also introduce a fitting procedure based on a fitting parameter – Fitting Error Index (FEI) – which permits us to know the quality of the results. In the experiments our model has achieved very high success rates (up to 90%) discriminating individuals in highly complex images acquired for us in real conditions in the Mediterranean Sea. Conclusions and future improvements to the proposed model are also discussed. [ABSTRACT FROM AUTHOR]

This paper evaluates the impact of using deep learning techniques in an automatic fish sizing process. Automatic fish sizing with a non-invasive approach involves working with different views of the fish's body and changing environments, being the stage of extraction of individuals in the image and the quality of the segmentation essential to obtain good sizing measurements. The goal of this work is to improve the results and functionality achieved in our previous studies with conventional segmentation methods based on local thresholding, where different limitations were observed, mainly the necessity of parameters tuning and a high computational cost. The number of detections must also increase significantly to increase the reliability of the statistical results. An approach using convolutional neural networks is proposed for fish detection and segmentation in videos acquired under real conditions, which eliminates the engineering procedure of parameter adjustment and generalises the solution for fish segmentation to deal with different environmental conditions (illumination and water turbidity) and background variability. The results show that the fish sizing procedure is enhanced thanks to the improvement in fish image instance segmentation. In particular, the number of fish measurements increases by up to 2.45 times when using Mask R-CNN and the PointRend module, thus increasing the accuracy of the fish length estimation, and the number of measurements per minute of computing time increases by up to 3.5 times. Our proposal obtains highly accurate fish length estimations in juvenile bluefin tuna based on a stereoscopic vision system and a deformable model of the fish's silhouette, both from the ventral and dorsal perspectives. An important improvement is achieved by applying CNN, as demonstrated by the number of segmented instances, the time required to segment an instance, and the accuracy of the fish sizing achieved. [ABSTRACT FROM AUTHOR]