Environmentally adaptive fish or no-fish classification for river video fish counters using high-performance desktop and embedded hardware

Soom, Jürgen; Pattanaik, Vishwajeet; Leier, Mairo; Tuhtan, Jeffrey A.

doi:10.1016/j.ecoinf.2022.101817

Cited by 10 publications

(3 citation statements)

References 35 publications

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“…Detection efficiency potentially depends on the intrinsic characteristics of the counting system and its interaction with the environmental conditions in which it is operated. Excessive turbidity, for example, can have a negative effect on the efficiency of video counting systems by altering the visibility of the counting system (Baumgartner et al, 2012;Soom et al, 2022). In contrast, acoustic cameras are notoriously insensitive to turbidity (Martignac et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

Diadromous fish run assessment: a double-observer model using acoustic cameras to correct imperfect detection and improve population abundance estimates

Boulenger,

Roussel,

Beaulaton

et al. 2024

Front. Ecol. Evol.

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IntroductionDiadromous fish populations have strongly declined over decades, and many species are protected through national and international regulations. They account for less than 1% of fish biodiversity worldwide, but they are among the most perceptible linkages between freshwater and marine ecosystems. During their migration back and forth, diadromous fish species are subjected to many anthropogenic threats, among which river damming can severely limit access to vital freshwater habitats and jeopardize population sustainability. Here, we developed a method based on a double-observer modeling approach for estimating the abundance of diadromous fish during their migration in rivers.MethodsThe method relies on two independent and synchronous records of fish counts that were analyzed jointly thanks to a hierarchical Bayesian model to estimate detection efficiencies and daily fish passage. We used simulated data to test model robustness and identify conditions under which the developed approach can be used. The approach was then applied to empirical data to estimate the annual silver eel run in the Touques River, France.ResultsThe analysis of simulated datasets and the study case gives evidence that the model can provide robust,accurate, and precise estimates of detection probabilities and total fish abundance in a set of conditions dependent on the information provided in the data (annual distribution of fish passage, annual number of observation, pairing period, etc.).DiscussionThen, the method can be applied to various species and counting systems, including nomad acoustic camera devices. We discuss its relevance for programs on river continuity restoration, notably to quantify population restoration associated with dam removals.

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Section: Discussionmentioning

confidence: 99%

Diadromous fish run assessment: a double-observer model using acoustic cameras to correct imperfect detection and improve population abundance estimates

Boulenger,

Roussel,

Beaulaton

et al. 2024

Front. Ecol. Evol.

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“…Despite the presence of many studies with different purposes and strategies, the number of studies concerning the real-time processing while tracking and counting the detected fish is very limited. In a study that is aimed to serve as a precursor to fish counting tasks, deep learning was used to classify the environmental conditions (Soom et al, 2022). According to the detected conditions, some traditional image processing methods were applied to the image to detect the presence/absence of fish.…”

Section: Related Workmentioning

confidence: 99%

Estimating catch rates in real time: Development of a deep learning based Nephrops (Nephrops norvegicus) counter for demersal trawl fisheries

2023

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Demersal trawling is largely a blind process where information on catch rates and compositions is only available once the catch is taken onboard the vessel. Obtaining quantitative information on catch rates of target species while fishing can improve a fisheries economic and environmental performance as fishers would be able to use this information to make informed decisions during fishing. Despite there are real-time underwater monitoring systems developed for this purpose, the video data produced by these systems is not analyzed in near real-time. In other words, the user is expected to watch the video feed continuously to evaluate catch rates and composition. This is obviously a demanding process in which quantification of the fish counts will be of a qualitative nature. In this study, underwater footages collected using an in-trawl video recording system were processed to detect, track, and count the number of individuals of the target species, Nephrops norvegicus, entering the trawl in real-time. The detection was accomplished using a You Only Look Once v4 (YOLOv4) algorithm. Two other variants of the YOLOv4 algorithm (tiny and scaled) were included in the study to compare their effects on the accuracy of the subsequent steps and overall speed of the processing. SORT algorithm was used as the tracker and any Nephrops that cross the horizontal level at 4/5 of the frame height were counted as catch. The detection performance of the YOLOv4 model provided a mean average precision (mAP@50) value of 97.82%, which is higher than the other two variants. However, the average processing speed of the tiny model is the highest with 253.51 frames per second. A correct count rate of 80.73% was achieved by YOLOv4 when the total number of Nephrops are considered in all the test videos. In conclusion, this approach was successful in processing underwater images in real time to determine the catch rates of the target species. The approach has great potential to process multiple species simultaneously in order to provide quantitative information not only on the target species but also bycatch and unwanted species to provide a comprehensive picture of the catch composition.

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“…If used in conjunction, the scanner and camera system can provide more reliable passage data for water bodies with few species and high turbidity compared to typical video systems [12,21]. In the near future, advances in artificial intelligence (AI) and machine learning (ML) can support automated analyses of videos [50].…”

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confidence: 99%

Monitoring of Fish Migration in Fishways and Rivers—The Infrared Fish Counter “Riverwatcher” as a Suitable Tool for Long-Term Monitoring

Haas,

Thumser,

Hellmair

et al. 2024

Water

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With the globally increasing awareness regarding the interconnectivity between freshwater ecosystems, projects for re-establishing connectivity with fishways as well as stock management are increasing. To ensure the quality and impact of such projects and for extending the scientific state of knowledge on this topic, a detailed monitoring of these measures is mandatory. Automatic, passive, and contactless counting devices can play a major role in these long-term monitoring projects, both for animal protection (non-invasive methods) and technical issues (comparatively low-cost systems in terms of time). Infrared fish counters can be used in a variety of fisheries applications and have proven particularly valuable in situations when long-term or continuous monitoring in lotic systems is required. Herein, we describe the function and technical capabilities of the VAKI Riverwatcher fish counter, provide information regarding its installation, and highlight some specific, practical applications of this technology. We discuss how the Riverwatcher can be used to validate the functionality of fish passage structures and to provide abundance estimates of migratory fish populations. Finally, we call attention to the challenges associated with operating monitoring equipment in variable river systems and briefly discuss potential sources of error that can influence the monitoring results and approaches to minimize or alleviate these errors.

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Environmentally adaptive fish or no-fish classification for river video fish counters using high-performance desktop and embedded hardware

Cited by 10 publications

References 35 publications

Diadromous fish run assessment: a double-observer model using acoustic cameras to correct imperfect detection and improve population abundance estimates

Diadromous fish run assessment: a double-observer model using acoustic cameras to correct imperfect detection and improve population abundance estimates

Estimating catch rates in real time: Development of a deep learning based Nephrops (Nephrops norvegicus) counter for demersal trawl fisheries

Monitoring of Fish Migration in Fishways and Rivers—The Infrared Fish Counter “Riverwatcher” as a Suitable Tool for Long-Term Monitoring

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