Underwater Fish Detection with Weak Multi-Domain Supervision

Given a sufficiently large training dataset, it is relatively easy to train a modern convolution neural network (CNN) as a required image classifier. However, for the task of fish classification and/or fish detection, if a CNN was trained to detect or classify particular fish species in particular background habitats, the same CNN exhibits much lower accuracy when applied to new/unseen fish species and/or fish habitats. Therefore, in practice, the CNN needs to be continuously fine-tuned to improve its classification accuracy to handle new project-specific fish species or habitats. In this work we present a labelling-efficient method of training a CNN-based fish-detector (the Xception CNN was used as the base) on relatively small numbers (4,000) of project-domain underwater fish/no-fish images from 20 different habitats. Additionally, 17,000 of known negative (that is, missing fish) general-domain (VOC2012) above-water images were used. Two publicly available fish-domain datasets supplied additional 27,000 of above-water and underwater positive/fish images. By using this multi-domain collection of images, the trained Xception-based binary (fish/not-fish) classifier achieved 0.17% false-positives and 0.61% false-negatives on the project's 20,000 negative and 16,000 positive holdout test images, respectively. The area under the ROC curve (AUC) was 99.94%.Comment: Published in the 2019 International Joint Conference on Neural Networks (IJCNN-2019), Budapest, Hungary, July 14-19, 2019, https://www.ijcnn.org/ , https://ieeexplore.ieee.org/document/885190

Autonomous temporal pseudo-labeling for fish detection

The first major step in training an object detection model to different classes from the available datasets is the gathering of meaningful and properly annotated data. This recurring task will determine the length of any project, and, more importantly, the quality of the resulting models. This obstacle is amplified when the data available for the new classes are scarce or incompatible, as in the case of fish detection in the open sea. This issue was tackled using a mixed and reversed approach: a network is initiated with a noisy dataset of the same species as our classes (fish), although in different scenarios and conditions (fish from Australian marine fauna), and we gathered the target footage (fish from Portuguese marine fauna; Atlantic Ocean) for the application without annotations. Using the temporal information of the detected objects and augmented techniques during later training, it was possible to generate highly accurate labels from our targeted footage. Furthermore, the data selection method retained the samples of each unique situation, filtering repetitive data, which would bias the training process. The obtained results validate the proposed method of automating the labeling processing, resorting directly to the final application as the source of training data. The presented method achieved a mean average precision of 93.11% on our own data, and 73.61% on unseen data, an increase of 24.65% and 25.53% over the baseline of the noisy dataset, respectively.info:eu-repo/semantics/publishedVersio

Comparison of Paper- and Electronic-Formatted Hydroacoustic Data Charts used for Salmon Enumeration on the Yukon River near Pilot Station

The Yukon River Sonar Project estimates salmon passage through the river near Pilot Station, Alaska. The hydroacoustic data collected by the sonar is currently printed on paper charts in a series of grey marks called “traces.” Technicians count traces that were generated by fish, and these numbers are used to calculate daily abundance estimates. New technology allows the hydroacoustic data to be presented on electronic charts viewed on a computer. The electronic charts also present the data in a series of grey marks, and fish traces must be identified manually by technicians. However, the electronic charts present the data in greater detail, and settings that are used to optimize the visibility of fish traces are more easily adjusted. Both of these features may improve fish detection, which would result in more accurate estimates. Project leaders are planning to make a complete switchover from paper to electronic charts. The principle aim of this study was to compare the fish counts produced by the paper and electronic formats in order to expose any biases and explain why they occur. Due to variation in the slope of the river bottom, the area of river covered by the sonar is divided into several horizontal strata by distance from the transducer. Due to the properties of sound and the variation in the shape of fish traces at different ranges, it is possible that the level and direction of bias may differ among strata. A sample of 150 electronic files, out of approximately 1,700, from the 2008 season was selected for this comparison. Files were counted using Echotastic, a program written by AYK Regional Sonar Biologist, Carl Pfisterer. The electronic chart counts were higher than the paper chart counts for strata one through four, while the electronic counts were lower than the paper counts for stratum five (linear regression output: stratum one: slope=1.112, y-intercept=44.662, stratum two: slope=1.344, y-intercept=13.615, stratum three: slope=1.098, y-intercept=-7.052, stratum four: slope=1.077, y-intercept=-8.566, stratum five: slope=0.827, y-intercept=-0.688). Both the positive and negative biases are likely a result of improved fish detection on the electronic charts and a high level of subjectivity associated with counting fish using sonar. If project leaders conclude that these biases are acceptable, a transition from paper to electronic charts would be advantageous, although correcting for differences will be necessary to make past and future fish estimates comparable

Reference Selection for an Active Ultrasound Wild Salmon Monitoring System

International audienceThis paper introduces the concept of automatic reference selection for active ultrasound wild salmon monitoring systems in turbulent underwater environments. A general in situ calibration procedure is proposed which allows grate improvements in terms of fish detection, identification and tracking capabilities

Echo location of fishes in Dhudawa reservoir (Madhya Pradesh)

An investigation was undertaken in order to locate fish using an echo sounder in Dhudawa Reservoir, Madhya Pradesh, India. In general, fish were found to be distributed either towards off-bottom or mid-water areas. Echo sounding is recommended for use in other reservoirs for fish detection

Multi-fish Detection And Tracking Using Track-mask Region Convolutional Neural Network

Deep learning has become more common in recent years due to its excellent results in many areas. This thesis primarily focuses on multi-fish detection and tracking methods in underwater videos. The existing multi-fish detection methods for underwater videos have a low detection rate and consumes time in the training and testing process due to the underwater conditions and the overfitting during training. Many multi-fish detection and tracking methods for underwater videos (based on deep learning) where low accuracy for multi-fish tracking and occlusion instances during multi-fish tracking leads to inability to distinguish edges, and inability to handle each detected object over time. Therefore, this research aims to improve and enhance methods for multi-fish detection and tracking in underwater videos based on the latest deep learning algorithms. The proposed improved multi-fish detection method involves three main steps: 1) Improving ResNet-101 backbone for better fish detection, 2) Enhancing the Region Proposal Network (RPN) method based on Faster R-CNN for multi-fish detection and 3) An improved multi-fish detection method in terms of accuracy and with a lower training and testing times by utilising the aforementioned methods. The proposed multi-fish tracking method (Track-Mask R-CNN) also exhibits similar enhanced characteristics compared to the state-of-art methods (using fish dataset). An accuracy of 86.7% and 78.9% have been achieved for the proposed multi-fish detection and tracking respectively

Fish detection automation from ARIS and DIDSON SONAR data

Abstract. The goal of this thesis is to analyse SONAR files produced by ARIS and DIDSON manufactured by Sound Metrics Co. which are ultrasonic, monostatic and multibeam echo-sounders. They are used to capture the behaviour of Atlantic salmon, which recently has been on the lists of endangered species. These SONARs can work in dark lighting conditions and provide high resolution images due to their high frequencies that ranges from 1.1 MHz to 1.8 MHz. The thesis goes through extracting data from file, redrawing it, and visualising it in human friendly format. Next, images are analysed to search for fish. Results of analysis are saved in formats such as JSON, to allow harmony with other legacy systems. Also the output helps in future development due to the support for JSON in multitude of programming languages. Eventually, a user-friendly user interface is introduced, which helps making the process easier. The software is tested against data-sets from rivers in Finland, that are rich in Atlantic salmon