1,046 research outputs found

    A Novel Detection Refinement Technique for Accurate Identification of Nephrops norvegicus Burrows in Underwater Imagery

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    With the evolution of the convolutional neural network (CNN), object detection in the underwater environment has gained a lot of attention. However, due to the complex nature of the underwater environment, generic CNN-based object detectors still face challenges in underwater object detection. These challenges include image blurring, texture distortion, color shift, and scale variation, which result in low precision and recall rates. To tackle this challenge, we propose a detection refinement algorithm based on spatial–temporal analysis to improve the performance of generic detectors by suppressing the false positives and recovering the missed detections in underwater videos. In the proposed work, we use state-of-the-art deep neural networks such as Inception, ResNet50, and ResNet101 to automatically classify and detect the Norway lobster Nephrops norvegicus burrows from underwater videos. Nephrops is one of the most important commercial species in Northeast Atlantic waters, and it lives in burrow systems that it builds itself on muddy bottoms. To evaluate the performance of proposed framework, we collected the data from the Gulf of Cadiz. From experiment results, we demonstrate that the proposed framework effectively suppresses false positives and recovers missed detections obtained from generic detectors. The mean average precision (mAP) gained a 10% increase with the proposed refinement technique.Versión del edito

    Automated classification of three-dimensional reconstructions of coral reefs using convolutional neural networks

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    © The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Hopkinson, B. M., King, A. C., Owen, D. P., Johnson-Roberson, M., Long, M. H., & Bhandarkar, S. M. Automated classification of three-dimensional reconstructions of coral reefs using convolutional neural networks. PLoS One, 15(3), (2020): e0230671, doi: 10.1371/journal.pone.0230671.Coral reefs are biologically diverse and structurally complex ecosystems, which have been severally affected by human actions. Consequently, there is a need for rapid ecological assessment of coral reefs, but current approaches require time consuming manual analysis, either during a dive survey or on images collected during a survey. Reef structural complexity is essential for ecological function but is challenging to measure and often relegated to simple metrics such as rugosity. Recent advances in computer vision and machine learning offer the potential to alleviate some of these limitations. We developed an approach to automatically classify 3D reconstructions of reef sections and assessed the accuracy of this approach. 3D reconstructions of reef sections were generated using commercial Structure-from-Motion software with images extracted from video surveys. To generate a 3D classified map, locations on the 3D reconstruction were mapped back into the original images to extract multiple views of the location. Several approaches were tested to merge information from multiple views of a point into a single classification, all of which used convolutional neural networks to classify or extract features from the images, but differ in the strategy employed for merging information. Approaches to merging information entailed voting, probability averaging, and a learned neural-network layer. All approaches performed similarly achieving overall classification accuracies of ~96% and >90% accuracy on most classes. With this high classification accuracy, these approaches are suitable for many ecological applications.This study was funded by grants from the Alfred P. Sloan Foundation (BMH, BR2014-049; https://sloan.org), and the National Science Foundation (MHL, OCE-1657727; https://www.nsf.gov). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript

    Supplementary report to the final report of the coral reef expert group: S6. Novel technologies in coral reef monitoring

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    [Extract] This report summarises a review of current technological advances applicable to coral reef monitoring, with a focus on the Great Barrier Reef Marine Park (the Marine Park). The potential of novel technologies to support coral reef monitoring within the Reef 2050 Integrated Monitoring and Reporting Program (RIMReP) framework was evaluated based on their performance, operational maturity and compatibility with traditional methods. Given the complexity of this evaluation, this exercise was systematically structured to address the capabilities of technologies in terms of spatial scales and ecological indicators, using a ranking system to classify expert recommendations.An accessible copy of this report is not yet available from this repository, please contact [email protected] for more information

    Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories

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    Corrección de una afiliación en Sensors 2023, 23, 16. https://doi.org/10.3390/s23010016An understanding of marine ecosystems and their biodiversity is relevant to sustainable use of the goods and services they offer. Since marine areas host complex ecosystems, it is important to develop spatially widespread monitoring networks capable of providing large amounts of multiparametric information, encompassing both biotic and abiotic variables, and describing the ecological dynamics of the observed species. In this context, imaging devices are valuable tools that complement other biological and oceanographic monitoring devices. Nevertheless, large amounts of images or movies cannot all be manually processed, and autonomous routines for recognizing the relevant content, classification, and tagging are urgently needed. In this work, we propose a pipeline for the analysis of visual data that integrates video/image annotation tools for defining, training, and validation of datasets with video/image enhancement and machine and deep learning approaches. Such a pipeline is required to achieve good performance in the recognition and classification tasks of mobile and sessile megafauna, in order to obtain integrated information on spatial distribution and temporal dynamics. A prototype implementation of the analysis pipeline is provided in the context of deep-sea videos taken by one of the fixed cameras at the LoVe Ocean Observatory network of Lofoten Islands (Norway) at 260 m depth, in the Barents Sea, which has shown good classification results on an independent test dataset with an accuracy value of 76.18% and an area under the curve (AUC) value of 87.59%.This work was developed within the framework of the Tecnoterra (ICM-CSIC/UPC) and the following project activities: ARIM (Autonomous Robotic Sea-Floor Infrastructure for Benthopelagic Monitoring; MarTERA ERA-Net Cofound) and RESBIO (TEC2017-87861-R; Ministerio de Ciencia, Innovación y Universidades)

    Ongoing monitoring of Tortugas Ecological Reserve: Assessing the consequences of reserve designation

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    Over the past five years, a biogeographic characterization of Tortugas Ecological Reserve(TER) has been carried out to measure the post-implementation effects of TER as a refuge for exploited species. Our results demonstrate that there is substantial microalgal biomass at depths between 10 and 30 m in the soft sediments at the coral reef interface, and that this community may play an important role in the food web supporting reef organisms. In addition, preliminary stable isotope data, in conjunction with prior results from the west Florida shelf, suggest that the shallow water benthic habitats surrounding the coral reefs of TER will prove to be an important source of the primary production ultimately fueling fish production throughout TER. The majority of the fish analyzed so far have exhibited a C isotope signature consistent with a food web which relies heavily on benthic primary production. Fish counts indicate a marked increase in the abundance of large fish (>20 cm) within the Reserve relative to the Out and Park strata, across years. Faunal collections from open and protected soft bottom habitat near the northern boundary of Tortugas North strongly suggest that relaxation of trawling pressure has increased benthic biomass and diversity in this area of TER. These data, employing an integrated Before - After Control Impact (BACI) design at multiple spatial scales, will allow us to continue to document and quantify the post-implementation effects of TER. (PDF contains 58 pages

    Empirical and mechanistic approaches to understanding and projecting change in coastal marine communities

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    This work details the effects of disturbance events on tropical coral reefs and highlights emerging techniques for improved monitoring and assessment of benthic change. The first chapter is in the form of a literature review, which aims to give a broad introduction to reef ecology, the impacts experienced by this system, and the methods used to monitor and assess change. The second chapter highlights a recently developed photogrammetric methodology which can be used to assess change in the marine environment. The methodology is then assessed for accuracy and comparability to standard benthic monitoring techniques. // The proceeding four chapters aim to address a number of ecological and management questions relating to reef community ecology, focussing on physical structure and demonstrating the utility of ‘Structure from Motion’ (SfM) photogrammetry as a monitoring and assessment tool. Chapters three and four more specifically use community managed small-scale Marine Protected Areas (MPAs) in the Philippines as a case study applying SfM, and assess the effectiveness of these MPAs. These chapters further highlight how physical changes can affect the function of the reefs and their associated fisheries. Chapters five and six then investigate how extreme climatic events can affect the structure and growth of reefs in the Indian Ocean, away from the array of confounding anthropogenic factors seen in the Philippines. // The final section looks to bring together these chapters to discuss the benefits of new technology, and the future of reefs under a changing climate

    Applying Object Detection to Marine Data and Exploring Explainability of a Fully Convolutional Neural Network Using Principal Component Analysis

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    With the rise of focus on man made changes to our planet and wildlife therein, more and more emphasis is put on sustainable and responsible gathering of resources. In an effort to preserve maritime wildlife the Norwegian government decided to create an overview of the presence and abundance of various species of marine lives in the Norwegian fjords and oceans. The current work evaluates the possibility of utilizing machine learning methods in particular the You Only Look Once version 3 algorithm to detect fish in challenging conditions characterized by low light, undesirable algae growth and high noise. It was found that the algorithm trained on images collected during the day time under natural light could detect fish successfully in images collected during night under artificial lighting. The overall average precision score of 88% was achieved. Later principal component analysis was used to analyze the features learned in different layers of the network. It is concluded that for the purpose of object detection in specific application areas, the network can be considerably simplified since many of the feature detector turns our to be redundant.acceptedVersio

    Habitat Associations and Reproduction of Fishes on the Northwestern Gulf of Mexico Shelf Edge

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    Several of the northwestern Gulf of Mexico (GOM) shelf-edge banks provide critical hard bottom habitat for coral and fish communities, supporting a wide diversity of ecologically and economically important species. These sites may be fish aggregation and spawning sites and provide important habitat for fish growth and reproduction. Already designated as habitat areas of particular concern, many of these banks are also under consideration for inclusion in the expansion of the Flower Garden Banks National Marine Sanctuary. This project aimed to gain a more comprehensive understanding of the communities and fish species on shelf-edge banks by way of gonad histology, baited remote underwater video, and hydroacoustics, as well as traditional statistical analyses, Bayesian estimation, and machine learning techniques. The study had several objectives: (1) estimate size at sexual transition for six GOM grouper species, (2) determine the optimal number of cameras on a baited remote underwater video system, (3) create a predictive model to provide presence of fish species based on habitat, and (4) grow a model to predict fish backscatter and density based on habitat parameters. Bayesian estimation allowed for size at sexual transition determinations for the six grouper species, outperforming the tradition frequentist models, especially for situations where tradition models failed to converge. Random forests based on video data had mixed results, but models for several species were able to predict fish presences with class and overall accuracies of greater than 80%. Boosted regression trees based on hydroacoustic data reinforced the importance of depth as a driving factor in fish distributions. The study provided greater understanding and predictive ability regarding fish on the bank habitats
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