Semisynthetic Versus Real-World Sonar Training Data for the Classification of Mine-Like Objects

Barngrover, Christopher; Kastner, Ryan; Belongie, Serge

doi:10.1109/joe.2013.2291634

Cited by 32 publications

(30 citation statements)

References 20 publications

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“…The data provide information on natural clutter contacts, whereas UXO contacts are then imported through simulations [5] [20]. The benefit of such an approach has been demonstrated on high-frequency side-scan sonar for the classification of minelike objects [19]. Furthermore, such an approach could also aid to resolve the problem of imbalanced data, i.e.…”

Section: Discussionmentioning

confidence: 99%

UXO Detector for Underwater Surveys Using Low-Frequency Sonar

Vossen¹,

Hunter²,

Duijster³

et al. 2015

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Section: Discussionmentioning

confidence: 99%

UXO Detector for Underwater Surveys Using Low-Frequency Sonar

Vossen¹,

Hunter²,

Duijster³

et al. 2015

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“…In another work [46], comparing the classifiers that use machine-learning approach and computer vision. The work corresponds to taking the aid of semisynthetic data for preprocessing of data before it is given as an input to machine-learning and computer vision approaches.…”

Section: Minesmentioning

confidence: 99%

“…This has a huge set of applications offered for nearshore surveillance such as detection of battle ships and arrival of logistics. [45] 1D Bay n/a n/a Few Acoustic Simulation [46] n/a Seabed n/a n/a n/a n/a Software Submarines [47] 2D Sea 110 m n/a n/a Acoustic Real-time [48] 4D Sea n/a Localization n/a n/a Real-time [ The GLINT10 [50] field test trials were performed in order to test the warfare surveillance. The work focuses on signal processing capabilities of the said project.…”

Section: Surveillancementioning

confidence: 99%

Underwater Sensor Network Applications: A Comprehensive Survey

Felemban

Shaikh

Qureshi

et al. 2015

International Journal of Distributed Sensor Networks

311

190

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There is no escaping fact that a huge amount of unexploited resources lies underwater which covers almost 70% of the Earth. Yet, the aquatic world has mainly been unaffected by the recent advances in the area of wireless sensor networks (WSNs) and their pervasive penetration in modern day research and industrial development. The current pace of research in the area of underwater sensor networks (UWSNs) is slow due to the difficulties arising in transferring the state-of-the-art WSNs to their underwater equivalent. Maximum underwater deployments rely on acoustics for enabling communication combined with special sensors having the capacity to take on harsh environment of the oceans. However, sensing and subsequent transmission tend to vary as per different subsea environments; for example, deep sea exploration requires altogether a different approach for communication as compared to shallow water communication. This paper particularly focuses on comprehensively gathering most recent developments in UWSN applications and their deployments. We have classified the underwater applications into five main classes, namely, monitoring, disaster, military, navigation, and sports, to cover the large spectrum of UWSN. The applications are further divided into relevant subclasses. We have also shown the challenges and opportunities faced by recent deployments of UWSN.

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“…Additionally, the Haar-like feature classifier in the next section was previously trained on a separate data set of 975 images each containing an MLO, with 426 truncated cones and 549 stealth wedges. This training data set was collected in the same manner and in the same fields of San Diego Bay [13].…”

Section: Data Setmentioning

confidence: 99%

“…The research paper by Viola and Jones was proposed as a face detector, but it has been applied to many other targets [9]- [11]. This concept has also been applied to MLO detection in sonar, but using a variation of boosting called GentleBoost [12], [13].…”

Section: Introductionmentioning

confidence: 99%

A Brain–Computer Interface (BCI) for the Detection of Mine-Like Objects in Sidescan Sonar Imagery

Barngrover

Althoff

DeGuzman³

et al. 2016

IEEE J. Oceanic Eng.

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Abstract-Detection of mine-like objects (MLOs) in sidescan sonar imagery is a problem that affects our military in terms of safety and cost. The current process involves large amounts of time for subject matter experts to analyze sonar images searching for MLOs. The automation of the detection process has been heavily researched over the years and some of these computer vision approaches have improved dramatically, providing substantial processing speed benefits. However, the human visual system has an unmatched ability to recognize objects of interest. This paper posits a brain-computer interface (BCI) approach, that combines the complementary benefits of computer vision and human vision. The first stage of the BCI, a Haar-like feature classifier, is cascaded in to the second stage, rapid serial visual presentation (RSVP) of images chips. The RSVP paradigm maximizes throughput while allowing an electroencephalography (EEG) interest classifier to determine the human subjects' recognition of objects. In an additional proposed BCI system we add a third stage that uses a trained support vector machine (SVM) based on the Haar-like features of stage one and the EEG interest scores of stage two. We characterize and show performance improvements for subsets of these BCI systems over the computer vision and human vision capabilities alone.Index Terms-Boosting, brain-computer interface (BCI), minelike object (MLO), object detection, rapid serial visual presentation (RSVP), sidescan sonar.

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Semisynthetic Versus Real-World Sonar Training Data for the Classification of Mine-Like Objects

Cited by 32 publications

References 20 publications

UXO Detector for Underwater Surveys Using Low-Frequency Sonar

UXO Detector for Underwater Surveys Using Low-Frequency Sonar

Underwater Sensor Network Applications: A Comprehensive Survey

A Brain–Computer Interface (BCI) for the Detection of Mine-Like Objects in Sidescan Sonar Imagery

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