Enhanced fish bending model for automatic tuna sizing using computer vision

Pau Muñoz‐Benavent

doi:10.1016/j.compag.2018.04.005

Enhanced fish bending model for automatic tuna sizing using computer vision

2018-07-01

66

PRE

AI

P

Pau Muñoz‐Benavent *

G

Gabriela Andreu-Garcı́a

J

José Miguel Valiente

A

Atienza-Vanacloig, V.

DOI：10.1016/j.compag.2018.04.005

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This paper presents a non-invasive fully automatic procedure to obtain highly accurate fish length estimation in adult Bluefin Tuna, based on a stereoscopic vision system and a deformable model of the fish ventral silhouette. The present work takes a geometric tuna model, which was previously developed by the same authors to discriminate fish in 2D images, and proposes new models to enhance the capabilities of the automatic procedure, from fish discrimination to accurate 3D length estimation. Fish length information is an important indicator of the health of wild fish stocks and for predicting biomass using length-weight relations. The proposal pays special attention to parts of the fish silhouette that have special relevance for accurate length estimation. The models have been designed to best fit the rear part of the fish, in particular the caudal peduncle, and a width parameter has been added to better fit the silhouette. Moreover, algorithms have been developed to extract snout tip and caudal peduncle features, allowing better initialization of model parameters. Snout Fork Length (SFL) measurements using the different models are extracted from images recorded with a stereoscopic vision system in a sea cage containing 312 adult Atlantic Bluefin Tuna. The automatic measurements are compared with two ground truths: one configured with semiautomatic measurements of favourable selected samples and one with real SFL measurements of the tuna stock collected at harvesting. Comparison with the semiautomatic measurements demonstrates that the combination of improved geometric models and feature extraction algorithms delivers good results in terms of fish length estimation error (up to 90% of the samples bounded in a 3% error margin) and number of automatic measurements (up to 950 samples out of 1000). When compared with real SFL measurements of the tuna stock, the system provides a high number of automatic detections (up to 6706 in a video of 135 min duration, i.e., 50 automatic measurements per minute of recording) and highly accurate length measurements, obtaining no statistically significant difference between automatic and real SFL frequency distributions. This procedure could be extended to other species to assess the size distribution of stocks, as discussed in the paper.

Keyword:

Underwater stereo-vision

Computer vision

Fisheries management

Automatic fish sizing

Biomass estimation

Enhanced fish bending model for automatic tuna sizing using computer vision

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