A Review of Underwater Mine Detection and Classification in Sonar Imagery

Hożyń, Stanisław

doi:10.3390/electronics10232943

Cited by 35 publications

(18 citation statements)

References 61 publications

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“…When designing the BUUV, it is essential to work on vehicle dynamics as well as on the description of the surroundings based on the sensor's parameters [4], [6]. Further, to ensure autonomy, the obstacle avoidance algorithm has to be implemented [8], [7].…”

Section: Introductionmentioning

confidence: 99%

Design, Modelling, And Simulation Of Biomimetic Underwater Vehicle

Piskur¹,

Larzewski²,

Kot³

et al. 2023

ECMS 2023 Proceedings Edited by Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni

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This paper discusses mathematical model implementations and simulation in various software environments for Unmanned Underwater Vehicles (UUVs), especially biomimetic ones. Gaining accurate simulation models of UUVs is challenging due to many nonlinear phenomena that need to be analysed. Further, the sensors’ accuracy and disturbances made by the natural water environment are difficult to predict during the simulation. On the other hand, an accurate simulation model is needed during new algorithm tests provided for increased vehicle autonomy. As a result, mathematical models and their implementation into different software are analysed and discussed in this paper. The model based on nonlinear differential equations is compared to an object-oriented, physical model based on Matlab Simscape Multibody.

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Section: Introductionmentioning

confidence: 99%

Design, Modelling, And Simulation Of Biomimetic Underwater Vehicle

Piskur¹,

Larzewski²,

Kot³

et al. 2023

ECMS 2023 Proceedings Edited by Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni

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show abstract

“…For example, bats use sound waves to detect the location of obstacles or prey. With the advancement of sensing technology, acoustic signals have been developed in various fields and are widely used in civil [20][21][22][23] and military [24][25][26] applications to detect and locate objects.…”

Section: Introductionmentioning

confidence: 99%

Sound Localization Based on Acoustic Source Using Multiple Microphone Array in an Indoor Environment

2022

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Sound signals have been widely applied in various fields. One of the popular applications is sound localization, where the location and direction of a sound source are determined by analyzing the sound signal. In this study, two microphone linear arrays were used to locate the sound source in an indoor environment. The TDOA is also designed to deal with the problem of delay in the reception of sound signals from two microphone arrays by using the generalized cross-correlation algorithm to calculate the TDOA. The proposed microphone array system with the algorithm can successfully estimate the sound source’s location. The test was performed in a standardized chamber. This experiment used two microphone arrays, each with two microphones. The experimental results prove that the proposed method can detect the sound source and obtain good performance with a position error of about 2.0~2.3 cm and angle error of about 0.74 degrees. Therefore, the experimental results demonstrate the feasibility of the system.

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“…According to previous research papers [ 1 ], sea mines are considered a strategic military tool to protect any country’s naval borders and can pose extreme danger for ships and submarines.…”

Section: Introductionmentioning

confidence: 99%

“…According to a previous study [ 1 ], DL algorithms, classified into supervised, semi-supervised, unsupervised and reinforcement learning, demand a vast quantity of data. Supervised learning trains a model with the categorized/labeled dataset to predict the mapping function, unsupervised learning identifies patterns in unlabeled/unclassified datasets, while semi-supervised learning combines a small number of labelled and a large number of unlabeled datasets.…”

Section: Introductionmentioning

confidence: 99%

“…Supervised learning trains a model with the categorized/labeled dataset to predict the mapping function, unsupervised learning identifies patterns in unlabeled/unclassified datasets, while semi-supervised learning combines a small number of labelled and a large number of unlabeled datasets. Reinforcement learning learns in an interactive environment by trial and error, using feedback from its own actions and experiences [ 1 ]. A previous paper [ 9 ] considers that deep learning approaches to automatic target recognition should be considered as follows: feature extraction (FE), classification, detection, and segmentation.…”

Section: Introductionmentioning

confidence: 99%

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Sea Mine Detection Framework Using YOLO, SSD and EfficientDet Deep Learning Models

Munteanu

Diana

Zamfir

et al. 2022

Sensors

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In the context of new geopolitical tensions due to the current armed conflicts, safety in terms of navigation has been threatened due to the large number of sea mines placed, in particular, within the sea conflict areas. Additionally, since a large number of mines have recently been reported to have drifted into the territories of the Black Sea countries such as Romania, Bulgaria Georgia and Turkey, which have intense commercial and tourism activities in their coastal areas, the safety of those economic activities is threatened by possible accidents that may occur due to the above-mentioned situation. The use of deep learning in a military operation is widespread, especially for combating drones and other killer robots. Therefore, the present research addresses the detection of floating and underwater sea mines using images recorded from cameras (taken from drones, submarines, ships and boats). Due to the low number of sea mine images, the current research used both an augmentation technique and synthetic image generation (by overlapping images with different types of mines over water backgrounds), and two datasets were built (for floating mines and for underwater mines). Three deep learning models, respectively, YOLOv5, SSD and EfficientDet (YOLOv5 and SSD for floating mines and YOLOv5 and EfficientDet for underwater mines), were trained and compared. In the context of using three algorithm models, YOLO, SSD and EfficientDet, the new generated system revealed high accuracy in object recognition, namely the detection of floating and anchored mines. Moreover, tests carried out on portable computing equipment, such as Raspberry Pi, illustrated the possibility of including such an application for real-time scenarios, with the time of 2 s per frame being improved if devices use high-performance cameras.

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A Review of Underwater Mine Detection and Classification in Sonar Imagery

Cited by 35 publications

References 61 publications

Design, Modelling, And Simulation Of Biomimetic Underwater Vehicle

Design, Modelling, And Simulation Of Biomimetic Underwater Vehicle

Sound Localization Based on Acoustic Source Using Multiple Microphone Array in an Indoor Environment

Sea Mine Detection Framework Using YOLO, SSD and EfficientDet Deep Learning Models

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