Towards automating underwater measurement of fish length: a comparison of semi-automatic and manual stereo–video measurements

Abstract

Underwater stereo–video systems are widely used for counting and measuring fish in aquaculture, fisheries, and conservation management. Length measurements are generated from stereo–video recordings by a software operator using a mouse to locate the head and tail of a fish in synchronized pairs of images. This data can be used to compare spatial and temporal changes in the mean length and biomass or frequency distributions of populations of fishes. Since the early 1990s stereo–video has also been used for measuring the lengths of fish in aquaculture for quota and farm management. However, the costs of the equipment, software, the time, and salary costs involved in post processing imagery manually and the subsequent delays in the availability of length information inhibit the adoption of this technology. We present a semi-automatic method for capturing stereo–video measurements to estimate the lengths of fish. We compare the time taken to make measurements of the same fish measured manually from stereo–video imagery to that measured semi-automatically. Using imagery recorded during transfers of Southern Bluefin Tuna (SBT) from tow cages to grow out cages, we demonstrate that the semi-automatic algorithm developed can obtain fork length measurements with an error of less than 1% of the true length and with at least a sixfold reduction in operator time in comparison to manual measurements. Of the 22 138 SBT recorded we were able to measure 52.6% (11 647) manually and 11.8% (2614) semi-automatically. For seven of the eight cage transfers recorde,d there were no statistical differences in the mean length, weight, or length frequency between manual and semi-automatic measurements. When the data were pooled across the eight cage transfers, there was no statistical difference in mean length or weight between the stereo–video-based manual and semi-automated measurements. Hence, the presented semi-automatic system can be deployed to significantly reduce the cost involved in adoption of stereo–video technology.

Automatic Bluefin Tuna sizing using a stereoscopic vision system

“…The need for a fully automatic system to accurately estimate the length of free swimming fish with a non-intrusive procedure has often been pointed out in recent years (Costa et al, 2009), (Zion, 2012), (Shortis et al, 2013), (Rosen et al 2013), (Williams & Lauffenburger, 2016), (Shafait et al, 2017). Fish length information is an important indicator of the health of wild fish stocks and for predicting biomass using length-weight relations (Lines et al, 2001), (Martinez-de Dios et al, 2003).…”

“…The most common mathematical model between fish length (L) and mass (W) is W = aL b , where a and b are parameters dependent on fish species (Zion, 2012) and on growth, in captivity or wild, (Aguado-Gimenez and Garcia-Garcia, 2005), (Katavić et al, 2016). The total biomass of a fish stock is commonly determined by obtaining the mean length of a statistically representative number of fish and counting the number of fish (Costa et al, 2009), (Shafait et al, 2017).…”

“…(Harvey et al, 2003) predicted the SFL of Southern Bluefin Tuna (SBT) inside a cage with a relative error of 0.16% (with SFL from 830 to 1412 mm), but harvesting only 54 SBT from thousands in the cage and measuring in a range of about 1 meter. (Shafait et al, 2017) present a semiautomatic method for estimating the fish lengths of 22138 SBT in transfers in a range from 1 to 4 metres, but it is not fully automatic and the results are compared with manual measurements and not ground truth data.…”

“…Fishing, climate change and pollution have high implications for fish stocks. Reliable fish measurements like length, height and width can be a very important indicator of the health of wild fish stocks (Dunbrack, 2006), (Shortis et al, 2013), (Rosen et al, 2013), (Shafait et al, 2017). Sampling methods to take fish measurements that involve capturing and handling live fish must be discarded, because they not only cause fish stress and possible death but also hinder the achievement of a large number of 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.…”