240 research outputs found
Survey on deep learning based computer vision for sonar imagery
Research on the automatic analysis of sonar images has focused on classical, i.e. non deep learning based, approaches for a long time. Over the past 15 years, however, the application of deep learning in this research field has constantly grown. This paper gives a broad overview of past and current research involving deep learning for feature extraction, classification, detection and segmentation of sidescan and synthetic aperture sonar imagery. Most research in this field has been directed towards the investigation of convolutional neural networks (CNN) for feature extraction and classification tasks, with the result that even small CNNs with up to four layers outperform conventional methods. The purpose of this work is twofold. On one hand, due to the quick development of deep learning it serves as an introduction for researchers, either just starting their work in this specific field or working on classical methods for the past years, and helps them to learn about the recent achievements. On the other hand, our main goal is to guide further research in this field by identifying main research gaps to bridge. We propose to leverage the research in this field by combining available data into an open source dataset as well as carrying out comparative studies on developed deep learning methods.Article number 10515711
SI-GAT: A method based on improved Graph Attention Network for sonar image classification
The existing sonar image classification methods based on deep learning are
often analyzed in Euclidean space, only considering the local image features.
For this reason, this paper presents a sonar classification method based on
improved Graph Attention Network (GAT), namely SI-GAT, which is applicable to
multiple types imaging sonar. This method quantifies the correlation
relationship between nodes based on the joint calculation of color proximity
and spatial proximity that represent the sonar characteristics in non-Euclidean
space, then the KNN (K-Nearest Neighbor) algorithm is used to determine the
neighborhood range and adjacency matrix in the graph attention mechanism, which
are jointly considered with the attention coefficient matrix to construct the
key part of the SI-GAT. This SI-GAT is superior to several CNN (Convolutional
Neural Network) methods based on Euclidean space through validation of real
data.Comment: 5 pages, 4 figure
Self-Supervised Learning for Improved Synthetic Aperture Sonar Target Recognition
This study explores the application of self-supervised learning (SSL) for
improved target recognition in synthetic aperture sonar (SAS) imagery. The
unique challenges of underwater environments make traditional computer vision
techniques, which rely heavily on optical camera imagery, less effective. SAS,
with its ability to generate high-resolution imagery, emerges as a preferred
choice for underwater imaging. However, the voluminous high-resolution SAS data
presents a significant challenge for labeling; a crucial step for training deep
neural networks (DNNs).
SSL, which enables models to learn features in data without the need for
labels, is proposed as a potential solution to the data labeling challenge in
SAS. The study evaluates the performance of two prominent SSL algorithms,
MoCov2 and BYOL, against the well-regarded supervised learning model, ResNet18,
for binary image classification tasks. The findings suggest that while both SSL
models can outperform a fully supervised model with access to a small number of
labels in a few-shot scenario, they do not exceed it when all the labels are
used.
The results underscore the potential of SSL as a viable alternative to
traditional supervised learning, capable of maintaining task performance while
reducing the time and costs associated with data labeling. The study also
contributes to the growing body of evidence supporting the use of SSL in remote
sensing and could stimulate further research in this area
Marine Robots for Underwater Surveillance
Abstract
Purpose of Review
The paper reviews the role of marine robots, in particular unmanned vehicles, in underwater surveillance, i.e. the control and monitoring of an area of competence aimed at identifying potential threats in support of homeland defence, antiterrorism, force protection and Explosive Ordnance Disposal (EOD).
Recent Findings
The paper explores separately robotic missions for identification and classification of threats lying on the seabed (e.g. EOD) and anti-intrusion robotic systems. The current main scientific challenge is identified in terms of enhancing autonomy and team/swarm mission capabilities by improving interoperability among robotic vehicles and providing communication networking capabilities, a non-trivial task, giving the severe limitations in bandwidth and latency of acoustic underwater messaging.
Summary
The work is intended to be a critical guide to the recent prolific bibliography on the topic, providing pointers to the main recent advancements in the field, and to give also a set of references in terms of mission and stakeholders' requirements (port authorities, coastal guards, navies)
Seabed classification using physics-based modeling and machine learning
In this work model-based methods are employed along with machine learning
techniques to classify sediments in oceanic environments based on the
geoacoustic properties of a two-layer seabed. Two different scenarios are
investigated. First, a simple low-frequency case is set up, where the acoustic
field is modeled with normal modes. Four different hypotheses are made for
seafloor sediment possibilities and these are explored using both various
machine learning techniques and a simple matched-field approach. For most noise
levels, the latter has an inferior performance to the machine learning methods.
Second, the high-frequency model of the scattering from a rough, two-layer
seafloor is considered. Again, four different sediment possibilities are
classified with machine learning. For higher accuracy, 1D Convolutional Neural
Networks (CNNs) are employed. In both cases we see that the machine learning
methods, both in simple and more complex formulations, lead to effective
sediment characterization. Our results assess the robustness to noise and model
misspecification of different classifiers
Reducing the false alarm rate of a simple sidescan sonar change detection system using deep learning
Unveiling the frontiers of deep learning: innovations shaping diverse domains
Deep learning (DL) enables the development of computer models that are
capable of learning, visualizing, optimizing, refining, and predicting data. In
recent years, DL has been applied in a range of fields, including audio-visual
data processing, agriculture, transportation prediction, natural language,
biomedicine, disaster management, bioinformatics, drug design, genomics, face
recognition, and ecology. To explore the current state of deep learning, it is
necessary to investigate the latest developments and applications of deep
learning in these disciplines. However, the literature is lacking in exploring
the applications of deep learning in all potential sectors. This paper thus
extensively investigates the potential applications of deep learning across all
major fields of study as well as the associated benefits and challenges. As
evidenced in the literature, DL exhibits accuracy in prediction and analysis,
makes it a powerful computational tool, and has the ability to articulate
itself and optimize, making it effective in processing data with no prior
training. Given its independence from training data, deep learning necessitates
massive amounts of data for effective analysis and processing, much like data
volume. To handle the challenge of compiling huge amounts of medical,
scientific, healthcare, and environmental data for use in deep learning, gated
architectures like LSTMs and GRUs can be utilized. For multimodal learning,
shared neurons in the neural network for all activities and specialized neurons
for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table
- …