Deep learning with self-supervision and uncertainty regularization to count fish in underwater images

Effective conservation actions require effective population monitoring. However, accurately counting animals in the wild to inform conservation decision-making is difficult. Monitoring populations through image sampling has made data collection cheaper, wide-reaching and less intrusive but created a need to process and analyse this data efficiently. Counting animals from such data is challenging, particularly when densely packed in noisy images. Attempting this manually is slow and expensive, while traditional computer vision methods are limited in their generalisability. Deep learning is the state-of-the-art method for many computer vision tasks, but it has yet to be properly explored to count animals. To this end, we employ deep learning, with a density-based regression approach, to count fish in low-resolution sonar images. We introduce a large dataset of sonar videos, deployed to record wild Lebranche mullet schools (Mugil liza), with a subset of 500 labelled images. We utilise abundant unlabelled data in a self-supervised task to improve the supervised counting task. For the first time in this context, by introducing uncertainty quantification, we improve model training and provide an accompanying measure of prediction uncertainty for more informed biological decision-making. Finally, we demonstrate the generalisability of our proposed counting framework through testing it on a recent benchmark dataset of high-resolution annotated underwater images from varying habitats (DeepFish). From experiments on both contrasting datasets, we demonstrate our network outperforms the few other deep learning models implemented for solving this task. By providing an open-source framework along with training data, our study puts forth an efficient deep learning template for crowd counting aquatic animals thereby contributing effective methods to assess natural populations from the ever-increasing visual data

Use of high resolution sonar for near-turbine fish observations (DIDSON) - We@Sea 2007-002

In this study we investigate small scale distribution of pelagic fish within a windfarm by means of a high resolution sonar (DIDSON, Dual frequency IDentification SONar; Soundmetrics). In addition we assess the bias of small scale variations induced by the effects of wind turbines (monopiles) on distribution of the pelagic fish community in the hydro acoustic surveys carried out on the OWEZ Near Shore Wind farm (NSW)

Mapping and Characterizing Subtidal Oyster Reefs Using Acoustic Techniques, Underwater Videography and Quadrat Counts

Populations of the eastern oyster Crassostrea virginica have been in long-term decline in most areas. A major hindrance to effective oyster management has been lack of a methodology for accurately and economically obtaining data on their distribution and abundance patterns. Here, we describe early results from studies aimed at development of a mapping and monitoring protocol involving acoustic techniques, underwater videography, and destructive sampling (excavated quadrats). Two subtidal reefs in Great Bay, New Hampshire, were mapped with side-scan sonar and with videography by systematically imaging multiple sampling cells in a grid covering the same areas. A single deployment was made in each cell, and a 5-10-s recording was made of a 0.25-m2 area; the location of each image was determined using a differential global position system. A still image was produced for each of the cells and all (n = 40 or 44) were combined into a single photomontage overlaid onto a geo-referenced base map for each reef using Arc View geographic information system. Quadrat (0.25 m2 ) samples were excavated from 9 or 10 of the imaged areas on each reef, and all live oysters were counted and measured. Intercomparisons of the acoustic, video, and quadrat data suggest: (1) acoustic techniques and systematic videography can readily delimit the boundaries of oyster reefs; (2) systematic videography can yield quantitative data on shell densities and information on reef structure; and (3) some combination of acoustics, systematic videography, and destructive sampling can provide spatially detailed information on oyster reef characteristics

Diel vertical migration strategies of zooplankton in oligotrophic Russell Pond, New Hampshire

Russell Pond is an ultra-oligotrophic lake with low chlorophyll a (1.9 mg L-1), total phosphorus (3.4 mg L-1), high Secchi Disk (10.4 m) and high light transmission (water coefficient of water, kw=0.33). Vertical migration of Chaoborus, Bosmina, Daphnia, and copepods were examined using net collections of zooplankton discrete depth counts and sonar. Three contrasting patterns of vertical migration were observed in Russell Pond. Chaoborus punctipennis larvae vertical migration began at 7 pm and migrated through the entire lake water column, a total of 23 m from the sediments to the surface water in less than 4 hours, Bosmina had an epilimnetic migration, moving 3 m upward toward the surface between 4 and 6 pm but did not migrate further at 8 pm. Daphnia migrated downward (reverse migration) nearly 4 m, and the copepods did not migrate vertically. In contrast to the other macrozooplankton, the calanoid copepods staved in the deep epilimnion, with no detectable vertical displacement. The variation in vertical migration patterns in Russell Pond illustrate how this adaptive diel behavior is tailored to the differing selective pressures on the different zooplankton species

Migration of Net Phytoplankton and Zooplankton in Mendum’s Pond, New Hampshire

The study examines the vertical distribution and migratory behavior of net phytoplankton and zooplankton of Mendum’s Pond in Barrington, N.H. The cyanobacteria, Microcystis and Aphanocapsa were the dominant net phytoplankton in this lake. Dominant zooplankton included Daphnia ambigua, Daphnia catawba, Bosmina longirostris, and both calanoid and cyclopoid copepods. Vertical distribution of net phytoplankton suggested migratory behavior, but no consistent pattern was observed. The zooplankton migrated nocturnally, however, calanoid copepods seemed to simultaneously migrate nocturnally and reversely at sunset, suggesting the presence of separate species or different age classes. Diel vertical migration (DVM) of zooplankton was not correlated with the distributions of net phytoplankton in the water column. However, grazing on smaller phytoplankton by zooplankton may have indirectly affected the abundance of the larger size class, net phytoplankton. SONAR analyses suggested that DVM of the phantom midge, Chaoborus, may have influenced the distribution of zooplankton. The findings suggest that a cascading effect of Chaoborus-zooplankton-phytoplankton may pressure vertical distributions of an entire ecosystem of planktonic organisms. Results from the study also raise concern in regard to abundant cyanobacteria and the future trophic status of Mendum’s Pond

Detecting fish aggregations from reef habitats mapped with high resolution side scan sonar imagery

As part of a multibeam and side scan sonar (SSS) benthic survey of the Marine Conservation District (MCD) south of St. Thomas, USVI and the seasonal closed areas in St. Croix—Lang Bank (LB) for red hind (Epinephelus guttatus) and the Mutton Snapper (MS) (Lutjanus analis) area—we extracted signals from water column targets that represent individual and aggregated fish over various benthic habitats encountered in the SSS imagery. The survey covered a total of 18 km2 throughout the federal jurisdiction fishery management areas. The complementary set of 28 habitat classification digital maps covered a total of 5,462.3 ha; MCDW (West) accounted for 45% of that area, and MCDE (East) 26%, LB 17%, and MS the remaining 13%. With the exception of MS, corals and gorgonians on consolidated habitats were significantly more abundant than submerged aquatic vegetation (SAV) on unconsolidated sediments or unconsolidated sediments. Continuous coral habitat was the most abundant consolidated habitat for both MCDW and MCDE (41% and 43% respectively). Consolidated habitats in LB and MS predominantly consisted of gorgonian plain habitat with 95% and 83% respectively. Coral limestone habitat was more abundant than coral patch habitat; it was found near the shelf break in MS, MCDW, and MCDE. Coral limestone and coral patch habitats only covered LB minimally. The high spatial resolution (0.15 m) of the acquired imagery allowed the detection of differing fish aggregation (FA) types. The largest FA densities were located at MCDW and MCDE over coral communities that occupy up to 70% of the bottom cover. Counts of unidentified swimming objects (USOs), likely representing individual fish, were similar among locations and occurred primarily over sand and shelf edge areas. Fish aggregation school sizes were significantly smaller at MS than the other three locations (MCDW, MCDE, and LB). This study shows the advantages of utilizing SSS in determining fish distributions and density

The value of remote sensing techniques in supporting effective extrapolation across multiple marine spatial scales

The reporting of ecological phenomena and environmental status routinely required point observations, collected with traditional sampling approaches to be extrapolated to larger reporting scales. This process encompasses difficulties that can quickly entrain significant errors. Remote sensing techniques offer insights and exceptional spatial coverage for observing the marine environment. This review provides guidance on (i) the structures and discontinuities inherent within the extrapolative process, (ii) how to extrapolate effectively across multiple spatial scales, and (iii) remote sensing techniques and data sets that can facilitate this process. This evaluation illustrates that remote sensing techniques are a critical component in extrapolation and likely to underpin the production of high-quality assessments of ecological phenomena and the regional reporting of environmental status. Ultimately, is it hoped that this guidance will aid the production of robust and consistent extrapolations that also make full use of the techniques and data sets that expedite this process

An assessment of plankton populations, toxic cyanobacteria, and potential impact of introduced marine alewife (Alosa pseudoharengus) in Pawtuckaway Lake, New Hampshire

A field study was conducted during the summer, 2005 to evaluate the lake water quality and planktonic communities in Pawtuckaway Lake, NH. Of special concern was the condition of the plankton populations since the lake had been subjected to introductions of adult sea-run alewife Overall water quality ranged from mesotrophic to eutrophic based on total phosphorus (8-31 !g L-1), chlorophyll a (max South, 5.0 !g L-1) and Secchi disk transparency (max North 5.1 m, min South 2.8 m). Of the three sites sampled, Fundy, North and South, Fundy (Zmax \u3c 2 m) did not stratify and had the highest concentrations of total phosphorus, followed by North and South sites, respectively. North and South sites stratified throughout the summer and developed anoxic hypolimnia, with the most severe oxygen deficit at the North site Potentially toxigenic cyanobacteria were detected at all three sites. Throughout the summer, the concentrations of the cyanotoxin microcystin in the lake were well above the average for NH lakes. Lakewater concentrations of microcystins exceeded WHO drinking water standards (1000 ng L-1) at the North site (1204.0 ng L-1) on July 21. The two dominant cyanobacteria were Anabaena spp.. and Microcystis aeruginosa. Oscillatoria (Planktothrix) were also present, but only rarely and therefore were probably were not responsible for most of the microcystins present in the lakewater

The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting

We present the Caltech Fish Counting Dataset (CFC), a large-scale dataset for detecting, tracking, and counting fish in sonar videos. We identify sonar videos as a rich source of data for advancing low signal-to-noise computer vision applications and tackling domain generalization in multiple-object tracking (MOT) and counting. In comparison to existing MOT and counting datasets, which are largely restricted to videos of people and vehicles in cities, CFC is sourced from a natural-world domain where targets are not easily resolvable and appearance features cannot be easily leveraged for target re-identification. With over half a million annotations in over 1,500 videos sourced from seven different sonar cameras, CFC allows researchers to train MOT and counting algorithms and evaluate generalization performance at unseen test locations. We perform extensive baseline experiments and identify key challenges and opportunities for advancing the state of the art in generalization in MOT and counting.Comment: ECCV 2022. 33 pages, 12 figure