Area-only method for underwater object tracking using autonomous vehicles

Masmitja, I.; Gomáriz, S.; Río, Joaquín Del; Kieft, Brian; O’Reilly, Tom; Aguzzi, Jacopo; Bouvet, Pierre-Jean; Fannjiang, Clara; Katija, Kakani

doi:10.1109/oceanse.2019.8867277

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…The smaller the MAE and RMSE, the better the performance, and the larger the CPR value, the better the model performance. These three indicators can intuitively reflect the closeness of the predicted result to the true value (Masmitja et al, 2020;Fonseca et al, 2022;Guzman et al, 2022;Skarsoulis et al, 2022).…”

Section: Prediction Performance Evaluationmentioning

confidence: 99%

Deep Learning for Marine Science

2024

Frontiers Research Topics

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Deep learning (DL), mainly composed of deep and complex neural networks such as recurrent network and convolutional network, is an emerging research branch in the field of artificial intelligence and machine learning. DL revolution has a far-reaching impact on all scientific disciplines and every corner of our lives. With continuing technological advances, marine science is entering into the big data era with the exponential growth of information. DL is an effective means of harnessing the power of big data. Combined with unprecedented data from cameras, acoustic recorders, satellite remote sensing, and large model outputs, DL enables scientists to solve complex problems in biology, ecosystems, climate, energy, as well as physical and chemical interactions. Although DL has made great strides, it is still only beginning to emerge in many fields of marine science, especially towards representative applications and best practices for the automatic analysis of marine organisms and marine environments. DL in nowadays' marine science mainly leverages cutting-edge techniques of deep neural networks and massive data which collected by <i>in-situ</i> optical or acoustic imaging sensors for underwater applications, such as plankton classification and coral reef detection. This research topic aims to expand the applications of marine science to cover all aspects of detection, classification, segmentation, localization, and density estimation of marine objects, organisms, and phenomena.

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Section: Prediction Performance Evaluationmentioning

confidence: 99%

Deep Learning for Marine Science

2024

Frontiers Research Topics

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Section: Prediction Performance Evaluationmentioning

confidence: 99%

An acoustic tracking model based on deep learning using two hydrophones and its reverberation transfer hypothesis, applied to whale tracking

Jin,

Xu,

Zhang

et al. 2023

Front. Mar. Sci.

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Acoustic tracking of whales’ underwater cruises is essential for protecting marine ecosystems. For cetacean conservationists, fewer hydrophones will provide more convenience in capturing high-mobility whale positions. Currently, it has been possible to use two hydrophones individually to accomplish direction finding or ranging. However, traditional methods only aim at estimating one of the spatial parameters and are susceptible to the detrimental effects of reverberation superimposition. To achieve complete whale tracking under reverberant interference, in this study, an intelligent acoustic tracking model (CIAT) is proposed, which allows both horizontal direction discrimination and distance/depth perception by mining unpredictable features of position information directly from the received signals of two hydrophones. Specifically, the horizontal direction is discriminated by an enhanced cross-spectral analysis to make full use of the exact frequency of received signals and eliminate the interference of non-source signals, and the distance/depth direction combines convolutional neural network (CNN) with transfer learning to address the adverse effects caused by unavoidable acoustic reflections and reverberation superposition. Experiments with real recordings show that 0.13 km/MAE is achieved within 8 km. Our work not only provides satisfactory prediction performance, but also effectively avoids the reverberation effect of long-distance signal propagation, opening up a new avenue for underwater target tracking.

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“…Non-extractive biological observations can be conducted in many ways using various modalities, including imaging, environmental DNA (or eDNA), and acoustics (Benoit-Bird and Lawson, 2016;Masmitja et al, 2020;Chavez et al, 2021). Of these modalities, imaging is the most direct approach, and its use has grown with various platforms, imaging systems, and sampling missions (Durden et al, 2016;Lombard et al, 2019).…”

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confidence: 99%

DeepSTARia: enabling autonomous, targeted observations of ocean life in the deep sea

Barnard,

Daniels,

Roberts

et al. 2024

Front. Mar. Sci.

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The ocean remains one of the least explored places on our planet, containing myriad life that are either unknown to science or poorly understood. Given the technological challenges and limited resources available for exploring this vast space, more targeted approaches are required to scale spatiotemporal observations and monitoring of ocean life. The promise of autonomous underwater vehicles to fulfill these needs has largely been hindered by their inability to adapt their behavior in real-time based on what they are observing. To overcome this challenge, we developed Deep Search and Tracking Autonomously with Robotics (DeepSTARia), a class of tracking-by-detection algorithms that integrate machine learning models with imaging and vehicle controllers to enable autonomous underwater vehicles to make targeted visual observations of ocean life. We show that these algorithms enable new, scalable sampling strategies that build on traditional operational modes, permitting more detailed (e.g., sharper imagery, temporal resolution) autonomous observations of underwater concepts without supervision and robust long-duration object tracking to observe animal behavior. This integration is critical to scale undersea exploration and represents a significant advance toward more intelligent approaches to understanding the ocean and its inhabitants.

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Area-only method for underwater object tracking using autonomous vehicles

Cited by 3 publications

References 18 publications

Deep Learning for Marine Science

Deep Learning for Marine Science

An acoustic tracking model based on deep learning using two hydrophones and its reverberation transfer hypothesis, applied to whale tracking

DeepSTARia: enabling autonomous, targeted observations of ocean life in the deep sea

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