Adaptive Management of living Marine Resources by integrating different data sources and key ecological parameters (ADMAR)

Joint US/Norway/Canada Workshop, Nantes, France. September 18th 2010

Modeling fish reaction to vessel noise, the significance of the reaction thresholds

A simple model of fish reaction to vessel noise is made. The fish are assumed to swim directly away from the noise source. The main noise source is assumed to be the propeller. Parameters for endurance and swimming speed are obtained from the literature. The initiating stimuli in the model are the loudness and/or the change in loudness. A sensitivity analysis is used to check the importance of the parameters. The model is very sensitive to vessel noise and the fish reaction thresholds. This is an artefact of the dB-scale used in the loudness measure. However, if the fish interpret the dB-scale as almost linear, this may also explain some of the variability in vessel avoidance problems. A small change in the reaction thresholds, may lead to significant changes in the resulting behaviour. If the task is to model fish reaction to vessels, emphasis should be put on the reaction thresholds and noise field around the vessel, rather than swimming speeds and endurance. In general the parameters describing the physiology are less sensitive than the parameters describing the behaviour

Correcting for avoidance in acoustic abundance estimates for herring using a generalized linear model

When a research vessel passes over a herring school or layer, the herring may avoid the vessel by swimming downwards and horizontally. The fish may also change its orientation, which may alter its mean target strength. Consequently, the echo abundance measured by the relatively narrow echo sounder beam does not always reflect the true density of the school. The fish reaction is strongest in the upper parts of the water column. This avoidance behaviour has been quantified in several experiments where a stationary, submerged transducer has been used to measure the changes in echo abundance during the passage of a survey vessel. In this paper two approaches for correcting the echo abundance for avoidance are investigated. The first approach is to correct the echo abundance in each depth layer separately; the second is to correct the total echo abundance, letting the correction depend on the mean depth of the fish at passing. In both approaches generalized linear models are fitted to the experimental data. Since the parameters are estimated with uncertainty, this uncertainty can be taken into account when the fitted models are used for correcting standard survey data

Evaluation of echosounder data preparation strategies for modern machine learning models

Fish stock assessment and management requires accurate estimates of fish abundance, which are typically derived from echosounder observations using acoustic target classification (ATC). Skilled operators are regularly assisted in classifying acoustic targets by software and there has been an increasing interest toward using machine learning to create improved tools. Recent studies have applied deep learning approaches to acoustic data, however, algorithm data-preparation strategies (influencing model output) are presently poorly understood and standardization is needed to enable collaborative research and management. For example, a common pre-processing technique is to resample backscatter data coming from echosounder measurements from the original resolution to a coarser resolution in the horizontal (time) and vertical (range) directions. Using data values derived from the volume backscattering coefficient obtained during the Norwegian sandeel survey, we investigate which resampling resolutions are suitable for ATC using a convolutional neural network trained to classify single values of backscatter data. This process is known as pixel-level semantic segmentation. Our results indicate that it is possible to downsample the data if important information related to acoustic characteristics is not smoothed out. We also show that the classification performance is improved when providing the network with contextual information relating to range. These findings will provide input to fisheries acoustic data standards and contribute to the on-going development of automated ATC methods.publishedVersio

Addressing class imbalance in deep learning for acoustic target classification

Acoustic surveys provide important data for fisheries management. During the surveys, ship-mounted echo sounders send acoustic signals into the water and measure the strength of the reflection, so-called backscatter. Acoustic target classification (ATC) aims to identify backscatter signals by categorizing them into specific groups, e.g. sandeel, mackerel, and background (as bottom and plankton). Convolutional neural networks typically perform well for ATC but fail in cases where the background class is similar to the foreground class. In this study, we discuss how to address the challenge of class imbalance in the sampling of training and validation data for deep convolutional neural networks. The proposed strategy seeks to equally sample areas containing all different classes while prioritizing background data that have similar characteristics to the foreground class. We investigate the performance of the proposed sampling methodology for ATC using a previously published deep convolutional neural network architecture on sandeel data. Our results demonstrate that utilizing this approach enables accurate target classification even when dealing with imbalanced data. This is particularly relevant for pixel-wise semantic segmentation tasks conducted on extensive datasets. The proposed methodology utilizes state-of-the-art deep learning techniques and ensures a systematic approach to data balancing, avoiding ad hoc methods.Addressing class imbalance in deep learning for acoustic target classificationpublishedVersio

A deep learning-based method to identify and count pelagic and mesopelagic fishes from trawl camera images

Fish counts and species information can be obtained from images taken within trawls, which enables trawl surveys to operate without extracting fish from their habitat, yields distribution data at fine scale for better interpretation of acoustic results, and can detect fish that are not retained in the catch due to mesh selection. To automate the process of image-based fish detection and identification, we trained a deep learning algorithm (RetinaNet) on images collected from the trawl-mounted Deep Vision camera system. In this study, we focused on the detection of blue whiting, Atlantic herring, Atlantic mackerel, and mesopelagic fishes from images collected in the Norwegian sea. To address the need for large amounts of annotated data to train these models, we used a combination of real and synthetic images, and obtained a mean average precision of 0.845 on a test set of 918 images. Regression models were used to compare predicted fish counts, which were derived from RetinaNet classification of fish in the individual image frames, with catch data collected at 20 trawl stations. We have automatically detected and counted fish from individual images, related these counts to the trawl catches, and discussed how to use this in regular trawl surveys.publishedVersio

How to obtain clear images from in-trawl cameras near the seabed? A case study from the Barents Sea demersal fishing grounds

Underwater camera systems are commonly used for monitoring fish and fishing gear behaviours. More recently, camera systems have been applied to scientific trawl surveys for improved spatial resolution and less invasive sampling and to commercial fisheries for better catch control and reduced by-catch. A challenge when using cameras in demersal trawls is poor image clarity due to the door and ground gear generated sediment plume. In this study we have measured the height of the sediment plume produced by a large commercial trawl in the Barents Sea using acoustic methods and investigated its effect on in-trawl camera image clarity. The trawl extension was lengthened, and additional buoyancy added to lift the camera system in the aft end of the trawl. The camera system was tested at increasing heights above seabed until no sediment plume was visible in the images. Based on the acoustic data the sediment plume was measured to be on average 4–5 m (SD 1.7 m) above sea floor. Image clarity improved significantly as the camera system clearance from seabed increased from 4 to 11 m. No effect of sediment type on image clarity was identified. The trawl modifications did not affect the trawl’s opening geometry or bottom contact. However, the increased length and angle of the under panel aft in the trawl and in the extension appears to have resulted in reduced water flow and may influence the passage and retention of fish. The feasibility of using camera systems in demersal trawls and this and other solutions for obtaining clear images are discussed.How to obtain clear images from in-trawl cameras near the seabed? A case study from the Barents Sea demersal fishing groundspublishedVersio

Deep Semi-Supervised Semantic Segmentation in Multi-Frequency Echosounder Data

Multi-frequency echosounder data can provide a broad understanding of the underwater environment in a non-invasive manner. The analysis of echosounder data is, hence, a topic of great importance for the marine ecosystem. Semantic segmentation, a deep learning based analysis method predicting the class attribute of each acoustic intensity, has recently been in the spotlight of the fisheries and aquatic industry since its result can be used to estimate the abundance of the marine organisms. However, a fundamental problem with current methods is the massive reliance on the availability of large amounts of annotated training data, which can only be acquired through expensive handcrafted annotation processes, making such approaches unrealistic in practice. As a solution to this challenge, we propose a novel approach, where we leverage a small amount of annotated data (supervised deep learning) and a large amount of readily available unannotated data (unsupervised learning), yielding a new data-efficient and accurate semi-supervised semantic segmentation method, all embodied into a single end-to-end trainable convolutional neural networks architecture. Our method is evaluated on representative data from a sandeel survey in the North Sea conducted by the Norwegian Institute of Marine Research. The rigorous experiments validate that our method achieves comparable results utilizing only 40 percent of the annotated data on which the supervised method is trained, by leveraging unannotated data. The code is available at https://github.com/SFI-Visual-Intelligence/PredKlus-semisup-segmentation.Deep Semi-Supervised Semantic Segmentation in Multi-Frequency Echosounder DatasubmittedVersionsubmittedVersionsubmittedVersio

Semi-supervised target classification in multi-frequency echosounder data

Acoustic target classification in multi-frequency echosounder data is a major interest for the marine ecosystem and fishery management since it can potentially estimate the abundance or biomass of the species. A key problem of current methods is the heavy dependence on the manual categorization of data samples. As a solution, we propose a novel semi-supervised deep learning method leveraging a few annotated data samples together with vast amounts of unannotated data samples, all in a single model. Specifically, two inter-connected objectives, namely, a clustering objective and a classification objective, optimize one shared convolutional neural network in an alternating manner. The clustering objective exploits the underlying structure of all data, both annotated and unannotated; the classification objective enforces a certain consistency to given classes using the few annotated data samples. We evaluate our classification method using echosounder data from the sandeel case study in the North Sea. In the semi-supervised setting with only a tenth of the training data annotated, our method achieves 67.6% accuracy, outperforming a conventional semi-supervised method by 7.0 percentage points. When applying the proposed method in a fully supervised setup, we achieve 74.7% accuracy, surpassing the standard supervised deep learning method by 4.7 percentage points.publishedVersio