A Study on Fish Classification Techniques using Convolutional Neural Networks on Highly Challenged Underwater Images

Underwater Fish Species Recognition (UFSR) has attained significance because of evolving research in underwater life. Manual techniques to distinguish fish can be tricky and tedious. They might require enormous inspecting endeavours, but they can be costly. It results in limited data and a lack of human resources, which may cause incorrect object identification. Automating the fish species detection and recognition utilizing technology would assist sea life science to evolve further. UFSR in wild natural habitats is difficult because the images open natural habitat, complex background, and low luminance. Species Visualization can assist us with deep knowledge of the movements of the species underwater. Automation systems can help to classify the fish accurately and consistently. Image classification has been emerging research with the advancement of deep learning systems. The reason is that the convolutional neural networks (CNNs) don't require explicit feature extraction methods. The vast majority of the current object detection and recognition mechanisms are based on images in the outdoor environment. This paper mainly reviews the strategies proposed in the past years for underwater fish detection and classification. Further, the paper also presents the classification of three different underwater datasets using CNN with evaluation metrics

ПРИМЕНЕНИЕ ГЛУБОКОГО ОБУЧЕНИЯ ДЛЯ АУГМЕНТАЦИИ И ГЕНЕРАЦИИ ПОДВОДНОГО НАБОРА ДАННЫХ С ПРОМЫШЛЕННЫМИ ОБЪЕКТАМИ

The purpose of the study: development of a deep learning method for augmentation and generation of a problem-oriented dataset containing industrial objects, including the development of more efficient data generation and augmentation algorithms based on deep learning, which allow you to create more diverse and realistic data corresponding to industrial objects that can be transferred from one style domain to another. The goal set in the study is related to the actual scientific and technical problem of providing computer vision in systems operating in the underwater environment. These can be autonomous uninhabited underwater vehicles that look for breaks in pipelines, analyze oil leaks, the movement of schools of fish, etc. However, today there is not enough data containing the described objects in the conditions of their real existence. Thus, it is necessary to provide the training sample with realistic images. Research methods: the CycleGAN architecture, which converts a dataset containing images of various objects taken in a laboratory or in a conventional aboveground environment into a dataset containing the cha¬racteristics of an underwater environment. To evaluate the developed augmentation algorithm, it is proposed to use image classification by domains, which can be performed using the ResNet convolutional neural network. Results of the study. A tool is presented to solve the problem of the lack of underwater datasets, a deep learning model is developed, which is used to create images with underwater elements. The model works on the principle of a cyclic generative adversarial network, which receives a real image of an industrial facility in surface conditions as an input, and returns a generated image of the same industrial facility in underwater conditions as an output. Visual analysis of images shows that this method is quite adequate. In addition, a test on the classification model showed almost 100% ability of the neural network to distinguish between domains. Conclusion. The study showed that the CycleGAN model can be used to create images of various objects in the underwater environment. In the future, it is possible to search for additional augmentation procedures, in addition, augmentations of the generated set can be used images, which will also provide researchers and developers with sufficient material with industrial facilities in the underwater environment. This can improve the quality of developments.Цель исследования: разработка метода глубокого обучения для аугментации и генерации проблемно-ориентированного набора данных, содержащего промышленные объекты, в том числе разработка более эффективных алгоритмов генерации и аугментации данных, основанных на глубоком обучении, которые позволяют создавать более разнообразные и реалистичные данные, соответствующие промышленным объектам, которые могут быть перенесены из одного стилевого домена в другой. Поставленная в исследовании цель связана с наличием актуальной научно-технической задачи обеспечения компьютерного зрения в системах, работающих в подводной среде. Это могут быть автономные необитаемые подводные аппараты, которые ищут прорывы в трубопроводах, анализируют утечку нефти, движение косяков рыб и т. п. Однако достаточного количества данных, содержащих описанные объекты в условиях их реального существования, сегодня нет. Таким образом, необходимо обеспечить обучающую выборку реалистичными изображениями. Методы исследования: архитектура CycleGAN, обеспечивающая преобразование набора данных, содержащего изображения различных объектов, сделанные в лабораторной или в обычной надземной среде, в набор данных, содержащий характеристики подводной среды. Для оценки разработанного алгоритма аугментации предлагается использовать классификацию изображений по доменам, которая может быть выполнена с помощью сверточной нейронной сети ResNet. Результаты исследования. Представлен инструмент для решения проблемы отсутствия подводных наборов данных, разработана модель глубокого обучения, которая применяется для создания изображений с подводными элементами. Модель работает по принципу циклической генеративно-состязательной сети, которая получает на вход реальное изображение промышленного объекта в надводных условиях, а на выход возвращает сгенерированное изображение того же промышленного объекта в подводных условиях. Визуальный анализ изображений показывает, что такой метод достаточно адекватен. Кроме того, проверка на классификационной модели показала почти 100%-ную способность нейросети различать домены. Заключение. Исследование показало, что модель CycleGAN можно использовать для создания изображений различных объектов в подводной среде. В будущем возможен поиск дополнительных процедур аугментации, кроме того, могут быть использованы аугментации сгенерированного набора изображений, что также обеспечит исследователей и разработчиков достаточным материалом с промышленными объектами в подводной среде. Это может повысить качество разработок

Seabed Surveillance and Underwater Structures Inspection with Remotely Operated Vehicle − Power Ray

The marine ecosystem is necessary to be monitored as it is exposed to externalities and pollutants that affect biodiversity and the state of the underwater structures. There is a demand for a better, more dynamic, and safe monitoring approach to underwater research and inspection. The unmanned underwater vehicles are becoming a reachable and intuitive tool for underwater inspection, such as for the inspection of the marine hull of vessels, bridges, foundations, piers, pylons, and other support structures in ports. The main advantage of the use of the remotely operated underwater drone is cost and time-efficiency, as they allow to obtain information in a fast and safe way in real-time. In this paper we investigate the possibility of the use of a remotely operated underwater drone Power Ray for seabed observation and underwater structures inspection. It describes the re-sults of the field research collected from the use of low-cost underwater drone Power Ray. The data collected with an underwater drone presents footages of different underwater structures and areas in order to document the seabed state and underwater structures. Additionally, this article provides an overview of the problems in underwater inspection and monitoring, and possibilities offered by remotely operated vehicle Power Ray in solv-ing them. The results of the paper are not unique to working with a low-cost drone, but are illustrative of the challenges and problems that new users are likely to encounter when using this technology

Fly-by-Pi: open source closed-loop control for geotechnical centrifuge testing applications

Geotechnical centrifuges are valuable instruments for physical modelling of complex geotechnical problems in a controlled laboratory setting. In comparison to full-scale testing, scaled models are cost effective to construct and instrument and, when tested in a geotechnical centrifuge at increased centrifugal accelerations, are capable of replicating full-scale stress–strain soil behaviour. Centrifuge modellers require specialised hardware and instruments capable of functioning under high accelerations. Such hardware is costly, nearly always purpose built, and often rely on commercial, closed-source data acquisition systems, hardware and control systems. This paper demonstrates a novel and versatile, low cost, open source logger and control system that works in parallel alongside existing centrifuge hardware. This solution, termed Fly-by-Pi, was developed using the Raspberry Pi microcomputer. The system provides closed-loop control of linear actuators with the ability to operate in either cyclic, monotonic, or static load- or displacement-control. The control mechanism can be reprogrammed according to experimental requirements, even during flight in the centrifuge. Three independent experiments are described which included the Fly-by-Pi controller as a key component in their operation. Based on the experience gained during these experiments, the authors encourage wide-spread adoption of open-sourced hardware solutions in extreme testing environments

Fly-by-Pi : open source closed-loop control for geotechnical centrifuge testing applications

Geotechnical centrifuges are valuable instruments for physical modelling of complex geotechnical problems in a controlled laboratory setting. In comparison to full-scale testing, scaled models are cost effective to construct and instrument and, when tested in a geotechnical centrifuge at increased centrifugal accelerations, are capable of replicating full-scale stress–strain soil behaviour. Centrifuge modellers require specialised hardware and instruments capable of functioning under high accelerations. Such hardware is costly, nearly always purpose built, and often rely on commercial, closed-source data acquisition systems, hardware and control systems. This paper demonstrates a novel and versatile, low cost, open source logger and control system that works in parallel alongside existing centrifuge hardware. This solution, termed Fly-by-Pi, was developed using the Raspberry Pi microcomputer. The system provides closed-loop control of linear actuators with the ability to operate in either cyclic, monotonic, or static load- or displacement-control. The control mechanism can be reprogrammed according to experimental requirements, even during flight in the centrifuge. Three independent experiments are described which included the Fly-by-Pi controller as a key component in their operation. Based on the experience gained during these experiments, the authors encourage wide-spread adoption of open-sourced hardware solutions in extreme testing environments.The UK Engineering and Physical Sciences Research Council (EPSRC) Global Challenges Fund under the Wind Africa project.http://www.elsevier.com/locate/ohxpm2021Civil Engineerin

Technoscience and the modernization of freshwater fisheries assessment and management

Inland fisheries assessment and management are challenging given the inherent com- plexity of working in diverse habitats (e.g., rivers, lakes, wetlands) that are dynamic on organisms that are often cryptic and where fishers are often highly mobile. Yet, technoscience is offering new tools that have the potential to reimagine how inland fisheries are assessed and managed. So-called ‘‘technoscience’’ refers to instances in which science and technology unfurl together, offering novel ways of spurring and achieving meaningful change. This paper considers the role of technoscience and its potential for modernizing the assessment and management of inland fisheries. It first explores technoscience and its potential benefits, followed by presentation of a series of synopses that explore the application (both successes and challenges) of new tech- nologies such as environmental DNA (eDNA), genomics, electronic tags, drones, phone apps, iEcology, and artificial intelligence to assessment and management. The paper also considers the challenges and barriers that exist in adopting new technologies. The paper concludes with a provocative assessment of the potential of technoscience to reform and modernize inland fisheries assessment and management. Although these tools are increasingly being embraced, there is a lack of platforms for aggregating these data streams and providing managers with actionable information in a timely manner. The ideas presented here should serve as a catalyst for beginning to work collectively and collaboratively towards fisheries assessment and management systems that harness the power of technology and serve to modernize inland fisheries management. Such transformation is urgently needed given the dynamic nature of environmental change, the evolving threat matrix facing inland waters, and the complex behavior of fishers. Quite simply, a dynamic world demands dynamic fisheries management; technoscience has made that within reach.publishedVersio

Разработка стенда физического подобия "Подводное техническое зрение"

Этот проект завершил разработку стенда подводного зрения, который можно использовать для решения задачи обнаружения мяча в подводной среде. Аппаратная часть этого проекта использует микроконтроллер для проектирования системы освещения, часть подводного видения завершает разработку алгоритма обнаружения мяча и разрабатывает алгоритм оптимизации изображения для оптимизации полученных подводных изображений для точного определения положения мяча под водой и другие информационные задачи. И разработка графический интерфейс для интеграции функций управления системой освещения, настройки оптимизации изображения и отображения результатов тестирования и т. д. , чтобы удобны для использования пользователями.This project has completed the design of the underwater vision bracket, which can be used to complete the ball detection task in the underwater environment. The hardware part of this project uses a microcontroller to design the lighting system; the underwater vision part completes the development of the ball detection algorithm, and develops an image optimization algorithm to optimize the acquired underwater images to accurately identify the position of the ball underwater. tasks such as information. And a GUI is designed to integrate the functions of lighting system control, adjustment of image optimization, and display of test results, which are convenient for users to use