Automatic Target Recognition for Mine Countermeasure Missions Using Forward-Looking Sonar Data

Palomeras, Narcís; Furfaro, Thomas C.; Williams, David P.; Carreras, Marc; Dugelay, Samantha

doi:10.1109/joe.2021.3103269

Cited by 33 publications

(7 citation statements)

References 39 publications

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“…Li et al (Li et al, 2021) proposed an underwater image enhancement network guided by mid projection, which utilizes multi-color space embedding and physical model learning methods to effectively improve visual quality. Palomeras et al (2022) proposed what was called ATR, which combines detectors and classifiers using a convolutional neural network model. Li et al (2022a) investigated first-person hand movement recognition for RGB-D sequences with eight classical pre-trained networks and one pre-trained network designed to extract RGB-D features.…”

Section: Image Object Recognitionmentioning

confidence: 99%

Multi-scale aware turbulence network for underwater object recognition

Zhou,

Cai,

Jia

et al. 2024

Front. Mar. Sci.

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Underwater imagery is subject to distortion, and the presence of turbulence in the fluid medium poses difficulties in accurately discerning objects. To tackle these challenges pertaining to feature extraction, this research paper presents a novel approach called the multi-scale aware turbulence network (MATNet) method for underwater object identification. More specifically, the paper introduces a module known as the multi-scale feature extraction pyramid network module, which incorporates dense linking strategies and position learning strategies to preprocess object contour features and texture features. This module facilitates the efficient extraction of multi-scale features, thereby enhancing the effectiveness of the identification process. Following that, the extracted features undergo refinement through comparison with positive and negative samples. Ultimately, the study introduces multi-scale object recognition techniques and establishes a multi-scale object recognition network for the precise identification of underwater objects, utilizing the enhanced multi-scale features. This process entails rectifying the distorted image and subsequently recognizing the rectified object. Extensive experiments conducted on an underwater distorted image enhancement dataset demonstrate that the proposed method surpasses state-of-the-art approaches in both qualitative and quantitative evaluations.

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Section: Image Object Recognitionmentioning

confidence: 99%

Multi-scale aware turbulence network for underwater object recognition

Zhou,

Cai,

Jia

et al. 2024

Front. Mar. Sci.

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“…한번 부설된 기뢰는 폭발하기 전까지 기뢰 존재여부 및 위치를 추 정하기 어려워 소해에 많은 위험이 동반되므로 기뢰탐 지연구에 대한 중요성이 부각되고 있다 [1] . 기뢰 탐지 에 대한 연구는 측면주사소나(Side Scan Sonar, SSS), 전방주시소나(Forward Looking Sonar, FLS)등으로 획득 된 소나 이미지를 이용하여 탐지하는 방법이 주로 연 구되었다 [2,3] . 국내에서는 전방주시소나의 하드웨어 개 발연구가 활발히 진행되고 있으나 [4] 사하다 [5] .…”

Section: 서 론unclassified

A Study on Mine Localization of Forward Looking Sonar Considering the Effect of Underwater Sound Refraction

Sul¹,

Oh²,

Yang³

et al. 2022

J. KIMS Technol

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Mine detection has been mainly studied with images of the forward-looking sonar. Forward-looking sonar assumes the propagation path of the sound wave as a straight path, creating the surrounding images. This might lead to errors in the detection by ignoring the refraction of the sound wave. In this study, we propose a mine localization method that can robustly identify the location of mines in an underwater environment by considering the refraction of sound waves. We propose a method of estimating the elevation angle of arrival of the target echo signal in a single receiver, and estimate the mine location by applying the estimated elevation angle of arrival to ray tracing. As a result of simulation, the method proposed in this paper was more effective in estimating the mine localization than the existing method that assumed the propagation path as a straight line.

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“…Sonars can also be used to detect dangerous objects. For example, in [ 9 ], the authors used a CNN-based approach to identify underwater mines lying on the seafloor. In pipeline following and inspection, sonars are also often employed.…”

Section: Introductionmentioning

confidence: 99%

Self Calibration of a Sonar–Vision System for Underwater Vehicles: A New Method and a Dataset

Pecheux

Creuze

Comby

et al. 2023

Sensors

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Monocular cameras and multibeam imaging sonars are common sensors of Unmanned Underwater Vehicles (UUV). In this paper, we propose a new method for calibrating a hybrid sonar–vision system. This method is based on motion comparisons between both images and allows us to compute the transformation matrix between the camera and the sonar and to estimate the camera’s focal length. The main advantage of our method lies in performing the calibration without any specific calibration pattern, while most other existing methods use physical targets. In this paper, we also propose a new sonar–vision dataset and use it to prove the validity of our calibration method.

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Automatic Target Recognition for Mine Countermeasure Missions Using Forward-Looking Sonar Data

Cited by 33 publications

References 39 publications

Multi-scale aware turbulence network for underwater object recognition

Multi-scale aware turbulence network for underwater object recognition

A Study on Mine Localization of Forward Looking Sonar Considering the Effect of Underwater Sound Refraction

Self Calibration of a Sonar–Vision System for Underwater Vehicles: A New Method and a Dataset

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