Procedures concerning preparations of autonomous underwater systems to operation focused on detection, classification and identification of mine like objects and ammunition

Żokowski, Mariusz; Chodnicki, Marcin; Krogulec, Przemysław; Sigiel, Norbert

doi:10.2478/jok-2018-0051

Cited by 3 publications

(2 citation statements)

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“…Nowadays, with very dynamic development of autonomous systems, we can discover new possibilities of detecting, classifying and identifying possible threat [28][29][30][31][32][33][34]. The use of autonomous vehicles for harbour and seaway traffic lines monitoring is one of the most effective solutions for maintaining security awareness.…”

Section: Discussionmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Wawrzyński

Zieja

Żokowski³

et al. 2022

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This article presents the information concerning aspects of the autonomous underwater vehicle (AUV) mission planning process, emphasizing maritime security monitoring and surveillance, and using side-looking sonars as a primary data source. The paper describes characteristic mission plan phases and gives suggestions for the operators, mainly concerning the safety and effectiveness of the AUV mission. The article describes the coverage path planning algorithm, which could be used to create an effective AUV mission plan, considering AUV manoeuvrability, sonar characteristics, and environmental factors. The results of the algorithms have been verified during the real mission of the AUV vehicle.

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Section: Discussionmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Wawrzyński

Zieja

Żokowski³

et al. 2022

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“…The first operators of Synthetic Aperture Sonar (SAS) were military users. In particular, they are used in large areas where small, low-signature bottom mines need to be detected and classified in a short amount of time and with sufficiently high resolution [3]. The technology also has a number of other potential applications, including seabed exploration, underwater archaeology, mapping debris, and search and rescue operations.…”

Section: Introductionmentioning

confidence: 99%

A Method for the Interpretation of Sonar Data Recorded during Autonomous Underwater Vehicle Missions

Zieja

Wawrzyński

Sigiel³

2022

Polish Maritime Research

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Image acquisition from autonomous underwater vehicles (AUVs) is useful for mapping objects on the seabed. However, there are few studies on the interpretation of data collected with side-scan sonar during autonomous underwater vehicle missions. By recording the seabed with 3D multibeam sonar, a large number of survey points can be obtained. The collected data are processed using applications based on remote sensing image processing. The data collected during AUV missions (or other sonar carriers) needs to be pre-processed to reach the proper effectiveness level. This process includes corrections of signal amplification (Time Varying Gain, or TVG) and geometric distortions of sonar images (Slant Range Corrections). It should be mentioned that, when carrying out the interpretation process for structures on the sea floor, sonar users need to understand the process of visualising seabed projections and depressions, as well as the resolution limitations of the sonar sensors.

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Automatic Classification of Unexploded Ordnance (UXO) Based on Deep Learning Neural Networks (DLNNS)

Sigiel,

Chodnicki,

Socik

et al. 2024

Polish Maritime Research

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This article discusses the use of a deep learning neural network (DLNN) as a tool to improve maritime safety by classifying the potential threat to shipping posed by unexploded ordnance (UXO) objects. Unexploded ordnance poses a huge threat to maritime users, which is why navies and non-governmental organisations (NGOs) around the world are using dedicated advanced technologies to counter this threat. The measures taken by navies include mine countermeasure units (MCMVs) and mine-hunting technology, which relies on the use of sonar imagery to detect and classify dangerous objects. The modern mine-hunting technique is generally divided into three stages: detection and classification, identification, and neutralisation/disposal. The detection and classification stage is usually carried out using sonar mounted on the hull of a ship or on an underwater vehicle. There is now a strong trend to intensify the use of more advanced technologies, such as synthetic aperture sonar (SAS) for high-resolution data collection. Once the sonar data has been collected, military personnel examine the images of the seabed to detect targets and classify them as mine-like objects (MILCO) or non mine-like objects (NON-MILCO). Computer-aided detection (CAD), computer-aided classification (CAC) and automatic target recognition (ATR) algorithms have been introduced to reduce the burden on the technical operator and reduce post-mission analysis time. This article describes a target classification solution using a DLNN-based approach that can significantly reduce the time required for post-mission data analysis during underwater reconnaissance operations.

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Procedures concerning preparations of autonomous underwater systems to operation focused on detection, classification and identification of mine like objects and ammunition

Cited by 3 publications

References 1 publication

Bulletin of the Polish Academy of Sciences: Technical Sciences

Bulletin of the Polish Academy of Sciences: Technical Sciences

A Method for the Interpretation of Sonar Data Recorded during Autonomous Underwater Vehicle Missions

Automatic Classification of Unexploded Ordnance (UXO) Based on Deep Learning Neural Networks (DLNNS)

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