Vision-based discrimination of tuna individuals in grow-out cages through a fish bending model

Abstract

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.

Intelligent feeding control methods in aquaculture with an emphasis on fish: a review

“…(Ghani & Isa ), but some algorithms have been developed to address these issues and determine the shape, size and orientation of swimming fish (Atienza‐Vanacloig et al . ; Hung et al . ).…”

“…Underwater images are usually of poor quality due to the limited range of visibility, low and uneven illumination, bright artefacts, noise, blurring, diminishing colour, etc. (Ghani & Isa 2015), but some algorithms have been developed to address these issues and determine the shape, size and orientation of swimming fish (Atienza-Vanacloig et al 2016;Hung et al 2016). Commercial video-based feeding systems have been used in aquaculture (AQ1SYSTEMS, 2017).…”

Automatic Bluefin Tuna sizing using a stereoscopic vision system

“…In the present paper, a variant of the tuna model presented in (Atienza-Vanacloig et al, 2016) was implemented to achieve our objective of estimating biometric measurements.…”

“…As indicated in (Harvey et al, 2003), collecting numerous, precise accurate data on length or age without the need to physically handle live fish has been identified as an urgent requirement for fisheries and aquaculture managers. Some authors, such as (Lines et al, 2001), (Zion, 2012), (Shortis et al, 2013), (Atienza-Vanacloig et al, 2016), (Shafait et al, 2017), highlight the need for fully automatic methods for underwater video processing.…”

“…So, robust fish detection methods dealing with these variations are required (Lines et al, 2001). In (Atienza-Vanacloig et al, 2016) a deformable adaptive model based on computer vision methods that automatically fit the body ventral silhouette of Bluefin Tuna while swimming was proposed. This model achieved very high success rates (up to 90%) identifying individuals in complex images acquired in real conditions.…”