Ship recognition on the sea surface using aerial images taken by Uav : a deep learning approach

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesOceans are very important for mankind, because they are a very important source of food, they have a very large impact on the global environmental equilibrium, and it is over the oceans that most of the world commerce is done. Thus, maritime surveillance and monitoring, in particular identifying the ships used, is of great importance to oversee activities like fishing, marine transportation, navigation in general, illegal border encroachment, and search and rescue operations. In this thesis, we used images obtained with Unmanned Aerial Vehicles (UAVs) over the Atlantic Ocean to identify what type of ship (if any) is present in a given location. Images generated from UAV cameras suffer from camera motion, scale variability, variability in the sea surface and sun glares. Extracting information from these images is challenging and is mostly done by human operators, but advances in computer vision technology and development of deep learning techniques in recent years have made it possible to do so automatically. We used four of the state-of-art pretrained deep learning network models, namely VGG16, Xception, ResNet and InceptionResNet trained on ImageNet dataset, modified their original structure using transfer learning based fine tuning techniques and then trained them on our dataset to create new models. We managed to achieve very high accuracy (99.6 to 99.9% correct classifications) when classifying the ships that appear on the images of our dataset. With such a high success rate (albeit at the cost of high computing power), we can proceed to implement these algorithms on maritime patrol UAVs, and thus improve Maritime Situational Awareness

Efficacy of Feedforward and LSTM Neural Networks at Predicting and Gap Filling Coastal Ocean Timeseries: Oxygen, Nutrients, and Temperature

Ocean data timeseries are vital for a diverse range of stakeholders (ranging from government, to industry, to academia) to underpin research, support decision making, and identify environmental change. However, continuous monitoring and observation of ocean variables is difficult and expensive. Moreover, since oceans are vast, observations are typically sparse in spatial and temporal resolution. In addition, the hostile ocean environment creates challenges for collecting and maintaining data sets, such as instrument malfunctions and servicing, often resulting in temporal gaps of varying lengths. Neural networks (NN) have proven effective in many diverse big data applications, but few oceanographic applications have been tested using modern frameworks and architectures. Therefore, here we demonstrate a “proof of concept” neural network application using a popular “off-the-shelf” framework called “TensorFlow” to predict subsurface ocean variables including dissolved oxygen and nutrient (nitrate, phosphate, and silicate) concentrations, and temperature timeseries and show how these models can be used successfully for gap filling data products. We achieved a final prediction accuracy of over 96% for oxygen and temperature, and mean squared errors (MSE) of 2.63, 0.0099, and 0.78, for nitrates, phosphates, and silicates, respectively. The temperature gap-filling was done with an innovative contextual Long Short-Term Memory (LSTM) NN that uses data before and after the gap as separate feature variables. We also demonstrate the application of a novel dropout based approach to approximate the Bayesian uncertainty of these temperature predictions. This Bayesian uncertainty is represented in the form of 100 monte carlo dropout estimates of the two longest gaps in the temperature timeseries from a model with 25% dropout in the input and recurrent LSTM connections. Throughout the study, we present the NN training process including the tuning of the large number of NN hyperparameters which could pose as a barrier to uptake among researchers and other oceanographic data users. Our models can be scaled up and applied operationally to provide consistent, gap-free data to all data users, thus encouraging data uptake for data-based decision making

Deep neural networks for automated detection of marine mammal species

Authors thank the Bureau of Ocean Energy Management for the funding of MARU deployments, Excelerate Energy Inc. for the funding of Autobuoy deployment, and Michael J. Weise of the US Office of Naval Research for support (N000141712867).Deep neural networks have advanced the field of detection and classification and allowed for effective identification of signals in challenging data sets. Numerous time-critical conservation needs may benefit from these methods. We developed and empirically studied a variety of deep neural networks to detect the vocalizations of endangered North Atlantic right whales (Eubalaena glacialis). We compared the performance of these deep architectures to that of traditional detection algorithms for the primary vocalization produced by this species, the upcall. We show that deep-learning architectures are capable of producing false-positive rates that are orders of magnitude lower than alternative algorithms while substantially increasing the ability to detect calls. We demonstrate that a deep neural network trained with recordings from a single geographic region recorded over a span of days is capable of generalizing well to data from multiple years and across the species’ range, and that the low false positives make the output of the algorithm amenable to quality control for verification. The deep neural networks we developed are relatively easy to implement with existing software, and may provide new insights applicable to the conservation of endangered species.Publisher PDFPeer reviewe

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

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)

Applications of deep learning in fish habitat monitoring: A tutorial and survey

Marine ecosystems and their fish habitats are becoming increasingly important due to their integral role in providing a valuable food source and conservation outcomes. Due to their remote and difficult to access nature, marine environments and fish habitats are often monitored using underwater cameras to record videos and images for understanding fish life and ecology, as well as for preserve the environment. There are currently many permanent underwater camera systems deployed at different places around the globe. In addition, there exists numerous studies that use temporary cameras to survey fish habitats. These cameras generate a massive volume of digital data, which cannot be efficiently analysed by current manual processing methods, which involve a human observer. Deep Learning (DL) is a cutting-edge Artificial Intelligence (AI) technology that has demonstrated unprecedented performance in analysing visual data. Despite its application to a myriad of domains, its use in underwater fish habitat monitoring remains under explored. In this paper, we provide a tutorial that covers the key concepts of DL, which help the reader grasp a high-level understanding of how DL works. The tutorial also explains a step-by-step procedure on how DL algorithms should be developed for challenging applications such as underwater fish monitoring. In addition, we provide a comprehensive survey of key deep learning techniques for fish habitat monitoring including classification, counting, localisation, and segmentation. Furthermore, we survey publicly available underwater fish datasets, and compare various DL techniques in the underwater fish monitoring domains. We also discuss some challenges and opportunities in the emerging field of deep learning for fish habitat processing. This paper is written to serve as a tutorial for marine scientists who would like to grasp a high-level understanding of DL, develop it for their applications by following our step-by-step tutorial, and see how it is evolving to facilitate their research efforts. At the same time, it is suitable for computer scientists who would like to survey state-of-the-art DL-based methodologies for fish habitat monitoring

Semi-supervised and weakly-supervised deep neural networks and dataset for fish detection in turbid underwater videos

Fish are key members of marine ecosystems, and they have a significant share in the healthy human diet. Besides, fish abundance is an excellent indicator of water quality, as they have adapted to various levels of oxygen, turbidity, nutrients, and pH. To detect various fish in underwater videos, Deep Neural Networks (DNNs) can be of great assistance. However, training DNNs is highly dependent on large, labeled datasets, while labeling fish in turbid underwater video frames is a laborious and time-consuming task, hindering the development of accurate and efficient models for fish detection. To address this problem, firstly, we have collected a dataset called FishInTurbidWater, which consists of a collection of video footage gathered from turbid waters, and quickly and weakly (i.e., giving higher priority to speed over accuracy) labeled them in a 4-times fast-forwarding software. Next, we designed and implemented a semi-supervised contrastive learning fish detection model that is self-supervised using unlabeled data, and then fine-tuned with a small fraction (20%) of our weakly labeled FishInTurbidWater data. At the next step, we trained, using our weakly labeled data, a novel weakly-supervised ensemble DNN with transfer learning from ImageNet. The results show that our semi-supervised contrastive model leads to more than 20 times faster turnaround time between dataset collection and result generation, with reasonably high accuracy (89%). At the same time, the proposed weakly-supervised ensemble model can detect fish in turbid waters with high (94%) accuracy, while still cutting the development time by a factor of four, compared to fully-supervised models trained on carefully labeled datasets. Our dataset and code are publicly available at the hyperlink FishInTurbidWater

Automatic Detection and Compression for Passive Acoustic Monitoring of the African Forest Elephant

In this work, we consider applying machine learning to the analysis and compression of audio signals in the context of monitoring elephants in sub-Saharan Africa. Earth's biodiversity is increasingly under threat by sources of anthropogenic change (e.g. resource extraction, land use change, and climate change) and surveying animal populations is critical for developing conservation strategies. However, manually monitoring tropical forests or deep oceans is intractable. For species that communicate acoustically, researchers have argued for placing audio recorders in the habitats as a cost-effective and non-invasive method, a strategy known as passive acoustic monitoring (PAM). In collaboration with conservation efforts, we construct a large labeled dataset of passive acoustic recordings of the African Forest Elephant via crowdsourcing, compromising thousands of hours of recordings in the wild. Using state-of-the-art techniques in artificial intelligence we improve upon previously proposed methods for passive acoustic monitoring for classification and segmentation. In real-time detection of elephant calls, network bandwidth quickly becomes a bottleneck and efficient ways to compress the data are needed. Most audio compression schemes are aimed at human listeners and are unsuitable for low-frequency elephant calls. To remedy this, we provide a novel end-to-end differentiable method for compression of audio signals that can be adapted to acoustic monitoring of any species and dramatically improves over naive coding strategies

Describing Polyps Behavior of a Deep-Sea Gorgonian, Placogorgia sp., Using a Deep-Learning Approach

Gorgonians play a fundamental role in the deep sea (below 200 m depth), composing three-dimensional habitats that are characterized by a high associated biodiversity and playing an important part in biogeochemical cycles. Here we describe the use of a benthic lander to monitoring polyps activity, used as a proxy of gorgonian feeding activity of three colonies of Placogorgia sp. Images cover a period of 22 days with a temporal resolution of 30 min. In addition, this seafloor observatory is instrumented with oceanographic sensors that allows continuous monitoring of the hydrographic conditions in the site. Deep-learning is used for automatic detection of the state of the polyps registered in the images. More than 1000 images of 3 large specimens of gorgonians are analyzed, annotating polyps as extended or retracted, using the semantic segmentation algorithm ConvNeXt. The segmentation results are used to describe the feeding patterns of this species. Placogorgia sp. shows a daily pattern of feeding conduct, depending on the hours of day and night. Using a Singular Spectrum Analysis approach, feeding activity is related to currents dynamics and Acoustic Doppler Current Profile (ADCP) return signal intensity, as proxy of suspended matter, achieving a linear correlation of 0.35 and 0.11 respectively. This is the first time that the behavior of the Placogorgia polyps, directly related to their feeding process, is described