Underwater Unexploded Ordnance (UXO) Classification Using a Matched Subspace Classifier With Adaptive Dictionaries

Hall, John J.; Azimi-Sadjadi, M.R.; Kargl, Steven G.; Zhao, Yinghui; Williams, Kevin L.

doi:10.1109/joe.2018.2835538

Cited by 31 publications

(16 citation statements)

References 23 publications

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“…Clearing sea from UXO is a new challenging task for governments. A rich literature in UXO detection explains the multiple advantages of using magnetic sensors [7]; recently, considering the heterogeneous nature of UXO, there are relevant studies that consider also acoustic sensors [8] and the reusage of already existing systems and algorithms for MCM purposes in order to perform UXO detection [9].…”

Section: Robots For Seabed Explorationmentioning

confidence: 99%

Marine Robots for Underwater Surveillance

et al. 2020

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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).

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Section: Robots For Seabed Explorationmentioning

confidence: 99%

Marine Robots for Underwater Surveillance

et al. 2020

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“…In (11), the solid angle β can be determined from cos β = cos θ cos θ s + sin θ sin θ s cos(φ s − φ).…”

Section: A Scattering and Inverse Scattering Imagingmentioning

confidence: 99%

“…The acoustic color image of the observed elastic waves can be generated by using fractional Fourier transform to separate each wave component [9] . After that, acoustic color images are also used for object classification and target identification [10], [11] because objects of different shapes or materials exhibit different scattering responses. Aside from object identification, the acoustic color images will help to determine the aspect angle of a cylinder [12] .…”

Section: Introductionmentioning

confidence: 99%

Sparse Reconstruction-Based Inverse Scattering Imaging in a Shallow Water Environment

2020

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Bistatic sonar or multistatic sonar system can collect more scattering information of targets than a monostatic sonar system. In this paper, sparse learning via iterative minimization method (SLIM) is introduced to distinguish wave components for time-domain (TD) back-propagation (BP) inverse scattering imaging improvement. Unlike the prevailing high central frequency (>100 kHz) and wideband imaging sonar systems, a relatively low-frequency band (1-10 kHz) is considered here. Due to the low sidelobe output of SLIM, the investigated object's surface in TD-BP image is much clearer in an ideal two-dimensional free field case. Furthermore, when the environmental information is known, this sparse reconstruction-based channel deconvolution method can be implemented to recognize, categorize the main propagating paths and then rectify their time of arrivals. Compared with the phase conjugation-based channel deconvolution method, the proposed approach's results have fewer sidelobes and higher signal-to-background ratio in the simulation.

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“…are applied to various object classifications including the classification of underwater objects. However, most researches on classification of underwater objects focus on the classification of underwater fishes [11], [12], corals [13], plankton [14], [15], unexploded ordnance and wrecks [16], etc [17], [18]. As far as garbage research is concerned, synthetic aperture radar technology [19] is often used to monitor marine debris.…”

Section: Introductionmentioning

confidence: 99%

Deep-Sea Debris Identification Using Deep Convolutional Neural Networks

Xue¹,

Huang²,

Chen³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Deep-sea debris is a globally growing problem that negatively impacting biological and chemical ecosystems. More seriously, the debris is likely to persist in the deep sea for long periods. Fortunately, with the help of the debris detection system the submersibles can clean up the debris. An excellent classifier is critical to the debris detection system. Therefore the objective of this study is to determine whether deep convolutional neural networks can distinguish the differences of debris and natural deep-sea environment, so as to effectively achieve deep-sea debris identification. First, a real deep-sea debris images (DDI) dataset is constructed for further classification research based on an online deep-sea debris database owned by the Japan Agency for Marine-Earth Science and Technology (JAMSTEC). Second, the hybrid Shuffle-Xception network is constructed to classify the deep-sea image as metal, glass, plastic, rubber, fishing net &rope, natural debris, and cloth. Furthermore, five common convolutional neural networks (CNNs) frameworks are also employed to implement the classification process. Finally, the identification experiments are carried out to validate the performance of the proposed methodology. The results demonstrate that the proposed method is superior to the state-of-art CNN methods and has the potential for deep-sea debris identification.

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Underwater Unexploded Ordnance (UXO) Classification Using a Matched Subspace Classifier With Adaptive Dictionaries

Cited by 31 publications

References 23 publications

Marine Robots for Underwater Surveillance

Marine Robots for Underwater Surveillance

Sparse Reconstruction-Based Inverse Scattering Imaging in a Shallow Water Environment

Deep-Sea Debris Identification Using Deep Convolutional Neural Networks

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