A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance

Domingos, Lucas C. F.; Santos, Paulo E.; Skelton, Phillip S. M.; Brinkworth, Russell S. A.; Sammut, Karl

doi:10.3390/s22062181

Cited by 26 publications

(9 citation statements)

References 138 publications

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“…SSD comprises a backbone model, typically a pre-trained image classification network serving as a feature extractor, and an SSD head, consisting of additional convolutional layers interpreting outputs as bounding boxes and object classes. YOLO, in contrast, generates multiple bounding boxes per object and suppresses redundant boxes to obtain final coordinates [92,93].…”

Section: Deep Learningmentioning

confidence: 99%

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A Survey of Seafloor Characterization and Mapping Techniques

Loureiro,

Dias,

Almeida

et al. 2024

Remote Sensing

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The deep seabed is composed of heterogeneous ecosystems, containing diverse habitats for marine life. Consequently, understanding the geological and ecological characteristics of the seabed’s features is a key step for many applications. The majority of approaches commonly use optical and acoustic sensors to address these tasks; however, each sensor has limitations associated with the underwater environment. This paper presents a survey of the main techniques and trends related to seabed characterization, highlighting approaches in three tasks: classification, detection, and segmentation. The bibliography is categorized into four approaches: statistics-based, classical machine learning, deep learning, and object-based image analysis. The differences between the techniques are presented, and the main challenges for deep sea research and potential directions of study are outlined.

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Section: Deep Learningmentioning

confidence: 99%

“…Subsequently, the second stage performs object classification. Examples of these architectures are the Fast R-CNN, Faster R-CNN, and Mask R-CNN [93,94].…”

Section: Deep Learningmentioning

confidence: 99%

A Survey of Seafloor Characterization and Mapping Techniques

Loureiro,

Dias,

Almeida

et al. 2024

Remote Sensing

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“…In recent years, many methods have attempted to establish end-to-end deep neural networks to identify underwater acoustic targets by automatically extracting deep features 1 . However, because of expensive annotation cost and inevitable annotation errors 2 , there is a lack of massive high-quality labeled samples to train robust deep neural networks in underwater acoustic target recognition 3 .…”

Section: Introductionmentioning

confidence: 99%

Few-shot learning for joint model in underwater acoustic target recognition

Tian,

Bai,

Zhou

et al. 2023

Sci Rep

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In underwater acoustic target recognition, there is a lack of massive high-quality labeled samples to train robust deep neural networks, and it is difficult to collect and annotate a large amount of base class data in advance unlike the image recognition field. Therefore, conventional few-shot learning methods are difficult to apply in underwater acoustic target recognition. In this report, following advanced self-supervised learning frameworks, a learning framework for underwater acoustic target recognition model with few samples is proposed. Meanwhile, a semi-supervised fine-tuning method is proposed to improve the fine-tuning performance by mining and labeling partial unlabeled samples based on the similarity of deep features. A set of small sample datasets with different amounts of labeled data are constructed, and the performance baselines of four underwater acoustic target recognition models are established based on these datasets. Compared with the baselines, using the proposed framework effectively improves the recognition effect of four models. Especially for the joint model, the recognition accuracy has increased by 2.04% to 12.14% compared with the baselines. The model performance on only 10 percent of the labeled data can exceed that on the full dataset, effectively reducing the dependence of model on the number of labeled samples. The problem of lack of labeled samples in underwater acoustic target recognition is alleviated.

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“…The (2, 1)-norm favors a small number of nonzero rows in the matrix W , therefore ensuring that the common features (most effective centers) will be selected. It should be noted that, Regularization techniques [10,11] proved to improve the generalization ability and therefore the performance of a model. A comprehensive study and a state-of-the-art review of the regularization strategies in machine learning is given in [10].…”

Section: Introductionmentioning

confidence: 99%

“…A comprehensive study and a state-of-the-art review of the regularization strategies in machine learning is given in [10]. It is being used in different classification problems such as, image recognition [12], Underwater Acoustic Data Classification [11] e.t.c. The above two approaches MDS and RMDS were for unsupervised data.…”

Section: Introductionmentioning

confidence: 99%

Supervised Regularized Multidimensional Scaling Using Weighted Stress Measure

Islam¹,

Bhuiyan²,

Jahan³

2022

ujam

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Supervised Regularized Multidimensional Scaling (SRMDS), a non-linear variant of classical Multi-Dimensional Scaling (cMDS) is proposed recently which involves Radial basis function. The method is focused on the effective selection of centers of the radial basis functions in transforming data from a higher dimensional space to a lower dimension. The transformation matrix is determined by minimizing stress. Weights of components of the stress that are of great importance for classification of data got less focus in Supervised Regularized Multidimensional Scaling. In this article, we have investigated several forms of non-linear functions which may be used as weights of the stress measure. A new form of Z-shape weight function dependent on intraclass information of the dataset is introduced which prefers to preserve global structure of the dataset. The efficiency of the proposed approach is illustrated on several benchmarking datasets which shows that the weighted Supervised Regularized Multidimensional Scaling (WSRMDS) outperforms the base method and some other state of the art approaches such as Linear Discriminant Analysis (LDA), and Supervised Principal Component Analysis (SPCA). Observing the finding of this research, among different weight functions, Z-shape weight function is recommended to use since it works better then any other weight functions for most of the data sets.

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A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance

Cited by 26 publications

References 138 publications

A Survey of Seafloor Characterization and Mapping Techniques

A Survey of Seafloor Characterization and Mapping Techniques

Few-shot learning for joint model in underwater acoustic target recognition

Supervised Regularized Multidimensional Scaling Using Weighted Stress Measure

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