Application of the YOLOv5 Model for the Detection of Microobjects in the Marine Environment

The efficiency of using the YOLOV5 machine learning model for solving the problem of automatic de-tection and recognition of micro-objects in the marine environment is studied. Samples of microplankton and microplastics were prepared, according to which a database of classified images was collected for training an image recognition neural network. The results of experiments using a trained network to find micro-objects in photo and video images in real time are presented. Experimental studies have shown high efficiency, comparable to manual recognition, of the proposed model in solving problems of detect-ing micro-objects in the marine environment.Comment: 9 pages, 8 figure

Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook

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Applications of Machine Learning in Chemical and Biological Oceanography

Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorize complex systems based on a large amount of data. ML is applied in various areas including natural science, engineering, space exploration, and even gaming development. This review focuses on the use of machine learning in the field of chemical and biological oceanography. In the prediction of global fixed nitrogen levels, partial carbon dioxide pressure, and other chemical properties, the application of ML is a promising tool. Machine learning is also utilized in the field of biological oceanography to detect planktonic forms from various images (i.e., microscopy, FlowCAM, and video recorders), spectrometers, and other signal processing techniques. Moreover, ML successfully classified the mammals using their acoustics, detecting endangered mammalian and fish species in a specific environment. Most importantly, using environmental data, the ML proved to be an effective method for predicting hypoxic conditions and harmful algal bloom events, an essential measurement in terms of environmental monitoring. Furthermore, machine learning was used to construct a number of databases for various species that will be useful to other researchers, and the creation of new algorithms will help the marine research community better comprehend the chemistry and biology of the ocean.Comment: 58 Pages, 5 Figure

Vision-based techniques for automatic marine plankton classification

Plankton are an important component of life on Earth. Since the 19th century, scientists have attempted to quantify species distributions using many techniques, such as direct counting, sizing, and classification with microscopes. Since then, extraordinary work has been performed regarding the development of plankton imaging systems, producing a massive backlog of images that await classification. Automatic image processing and classification approaches are opening new avenues for avoiding time-consuming manual procedures. While some algorithms have been adapted from many other applications for use with plankton, other exciting techniques have been developed exclusively for this issue. Achieving higher accuracy than that of human taxonomists is not yet possible, but an expeditious analysis is essential for discovering the world beyond plankton. Recent studies have shown the imminent development of real-time, in situ plankton image classification systems, which have only been slowed down by the complex implementations of algorithms on low-power processing hardware. This article compiles the techniques that have been proposed for classifying marine plankton, focusing on automatic methods that utilize image processing, from the beginnings of this field to the present day.Funding for open access charge: Universidad de Málaga / CBUA. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. The authors wish to thank Alonso Hernández-Guerra for his frm support in the development of oceanographic technology. Special thanks to Laia Armengol for her help in the domain of plankton. This study has been funded by Feder of the UE through the RES-COAST Mac-Interreg pro ject (MAC2/3.5b/314). We also acknowledge the European Union projects SUMMER (Grant Agreement 817806) and TRIATLAS (Grant Agreement 817578) from the Horizon 2020 Research and Innovation Programme and the Ministry of Science from the Spanish Government through the Project DESAFÍO (PID2020-118118RB-I00)

Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook

The deep learning (DL) revolution is touching all scientific disciplines and corners of our lives as a means of harnessing the power of big data. Marine ecology is no exception. New methods provide analysis of data from sensors, cameras, and acoustic recorders, even in real time, in ways that are reproducible and rapid. Off-the-shelf algorithms find, count, and classify species from digital images or video and detect cryptic patterns in noisy data. These endeavours require collaboration across ecological and data science disciplines, which can be challenging to initiate. To promote the use of DL towards ecosystem-based management of the sea, this paper aims to bridge the gap between marine ecologists and computer scientists. We provide insight into popular DL approaches for ecological data analysis, focusing on supervised learning techniques with deep neural networks, and illustrate challenges and opportunities through established and emerging applications of DL to marine ecology. We present case studies on plankton, fish, marine mammals, pollution, and nutrient cycling that involve object detection, classification, tracking, and segmentation of visualized data. We conclude with a broad outlook of the field’s opportunities and challenges, including potential technological advances and issues with managing complex data sets.publishedVersionPaid Open Acces

Machine learning techniques to characterize functional traits of plankton from image data

Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms

Restaurierung von kohärenten Bildern

In this thesis a series of novel algorithms for high quality restoration of coherent images is introduced. This task cannot be solved with established methods for the restoration of incoherent images. These algorithms focus on the correction of images in coherent imaging systems with a-priori known aberrations. The new wavefront correction algorithms achieve a significantly higher restoration quality than any previously known technique. The algorithms in this thesis are based on latest advances in optimization algorithms, particularly projections onto convex sets, proximal optimization and fractal self-similarity. Convergence and performance of the individual algorithms are analyzed in detail in various scenarios on real and simulated images. The evaluation also deals with the impact of noise on the restoration quality. Practical application of the new algorithms on microscopic images of diverse biological and human samples, as well as shadowgraph images of plankton acquired with a laboratory setup prove their efficiency. The new algorithms also have promising future applications in other areas, for example in adaptive optics and astronomy.In dieser Thesis werden mehrere neue Algorithmen für eine qualitativ hochwertige Restaurierung von kohärenten Bildern vorgestellt. Diese Aufgabe kann mit den bekannten Methoden für die Restaurierung von nicht kohärenten Bildern nicht gelöst werden. Die neuen Algorithmen sind auf die Wiederherstellung von Bildern in kohärenten Abbildungssystemen, bei denen die Aberrationen a-priori bekannt sind, ausgerichtet. Sie dienen der Korrektur der Wellenfront und erreichen eine wesentlich höhere Qualität der Bildrekonstruktion als sämtliche vorbekannte Verfahren. Die Algorithmen in dieser Thesis basieren auf neuesten Optimierungsalgorithmen, wie Projektionen in konvexe Sets, proximale Optimierung und fraktaler Ähnlichkeit. Die Konvergenz und Leistung der einzelnen Algorithmen wird ausführlich in unterschiedlichen Szenarien mit simulierten und realen Bildern untersucht. Eine praktische Erprobung der neuen Algorithmen an mikroskopischen Aufnahmen von unterschiedlichen biologischen und humanen Proben, wie auch an Aufnahmen vom Shadowgraph, bestätigt ihre Effizienz. Die neuen Algorithmen haben vielversprechende künftige Anwendungen, auch in anderen Gebieten, z.B. in der adaptiven Optik und der Astronomie

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens

Remote Sensing of the Oceans

This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements