A Framework for Acoustic Segmentation Using Order Statistic-Constant False Alarm Rate in Two Dimensions From Sidescan Sonar Data

Villar, Sebastián A.; Paula, Mariano De; Solari, Franco J.; Acosta, Gerardo

doi:10.1109/joe.2017.2721058

Cited by 16 publications

(11 citation statements)

References 25 publications

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“…Therefore, this technique considers the contextual information of each cell under test

x_{i, j}

to determine the adaptive detection threshold. Then, for each test cell, the detector system makes a decision according to the following decision strategy: For further details about this, please refer to [12, 15, 16]

x_{i, j} ≷_{H_{0}}^{H_{1}} \overset{false^}{T} .

…”

Section: Target Detection Using Os‐cfar 2dmentioning

confidence: 99%

“…Besides, OS-CFAR is suitable for being used in multi-target situations because of its high resolution [17][18][19]22]. In sonar OS-CFAR demonstrated to be more robust in multi-target situations and less sensitive to the presence of the speckle noise [13][14][15][16]. The main drawback of OS-CFAR is its computational effort because sorting is a timeconsuming task.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the constant false alarm rate (CFAR) represents an adaptive technique able to perform accurate and robust target detection. This technique is commonly used in radar technology for detecting moving objects [9, 10] as well also in sonar technology applied in acoustic images from different sonar devices for multi‐target detection [11–13], underwater pipeline detection on the seafloor [14, 15], acoustic segmentation of several types of regions [16], among others. CFAR calculates an adaptive detection threshold from interference power values to maintain an expected false alarm probability [9].…”

Section: Introductionmentioning

confidence: 99%

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Efficient approach for OS‐CFAR 2D technique using distributive histograms and breakdown point optimal concept applied to acoustic images

Villar

Menna

Torcida³

et al. 2019

IET Radar, Sonar & Navigation

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In this work, a new approach to improve the algorithmic efficiency of the Order Statistic-Constant False Alarm Rate (OS-CFAR) applied in two dimensions (2D) is presented. OS-CFAR is widely used in radar technology for detecting moving objects as well as in sonar technology for the relevant areas of segmentation and multi-target detection on the seafloor. OS-CFAR rank orders the samples obtained from a sliding window around a test cell to select a representative sample that is used to calculate an adaptive detection threshold maintaining a false alarm probability. Then, the test cell is evaluated to determine the presence or absence of a target based on the calculated threshold. The rank orders allows that OS-CFAR technique to be more robust in multi-target situations and less sensitive than other methods to the presence of the speckle noise, but requires higher computational effort. This is the bottleneck of the technique. Consequently, the contribution of this work is to improve the OS-CFAR 2D with the distributive histograms and the optimal breakdown point optimal concept, mainly from the standpoint of efficient computation. In this way, the OS-CFAR 2D on-line computation was improved, by means of speeding up the samples sorting problem through the improvement in the calculus of the statistics order. The theoretical algorithm analysis is presented to demonstrate the improvement of this approach. Also, this novel efficient OS-CFAR 2D was contrasted experimentally on acoustic images.

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“…Therefore, this technique considers the contextual information of each cell under test

x_{i, j}

x_{i, j} ≷_{H_{0}}^{H_{1}} \overset{false^}{T} .

…”

Section: Target Detection Using Os‐cfar 2dmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient approach for OS‐CFAR 2D technique using distributive histograms and breakdown point optimal concept applied to acoustic images

Villar

Menna

Torcida³

et al. 2019

IET Radar, Sonar & Navigation

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“…Esta primera clasificación se realiza en este módulo mediante dos subsistemas, uno de seguimiento (Tracking Subsystem -TSS) y otro de detección de obstáculos (Obstacle Detection Subsystem -ODSS [12]). El TSS maneja los datos provenientes de ecosondas en el caso de realización de batimetrías, o de cualquier otro sensor de acuerdo a la aplicación, y debe ser capaz de realizar detecciones en tiempo eficiente [13][14]. El ODSS actual-mente emplea los datos provenientes de la ecosonda mecánicamente escaneada (Micron Tritech© de la Fig.…”

Section: Sistema De Percepción -Psunclassified

MACÁBOT: Prototipo de Vehículo Autónomo de Superficie (ASV)

Leegstra

Paula

Carlucho

et al. 2019

RTyC

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En este artículo se presentan las decisiones de diseño y construcción de un robot móvil autónomo que navega en la superficie acuática, conocido como vehículo autónomo de superficie (ASV por sus siglas en inglés). Fue casi totalmente desarrollado y armado en los laboratorios del Núcleo INTELYMEC-CIFICEN, de la Universidad Nacional del Centro de la Prov. de Buenos Aires, Argentina en cooperación con una empresa interesada en su comercialización. Este prototipo, llamado MACÁBOT ha sido exitosamente probado en aguas calmas. Desde el punto de vista de I+D, se lo empleará como una plataforma experimental para nuevos algoritmos y tecnología subacuática. Su construcción mecánica, la electrónica de bajo nivel, como también su arquitectura de software es explicada. También se presentan las primeras pruebas acuáticas con una discusión sobre las mismas.

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“…height of the sonar from the seabed cannot be accurately obtained [7]. Inaccurate sonar heights will lead to inaccurate geocoding sonar images [8], confuse the water column information with the seabed information, and cause serious problems in applications of target recognition and segmentation [9][10][11], image interpretation [12,13], and seabed sediment classification [14][15][16]. The bottom tracking of side scan data can accurately obtain the sonar height from the seabed by finding the first echo that reaches the seabed.…”

mentioning

confidence: 99%

Real-Time Bottom Tracking Using Side Scan Sonar Data Through One-Dimensional Convolutional Neural Networks

2019

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As one of the most commonly used acoustic systems in seabed surveys, the altitude of the side scan sonar from the seafloor is always difficult to determine, especially when raw signal levels and gain information are unavailable. The inaccurate sonar altitudes would limit the applications of sonar image geocoding, target detection, and sediment classification. The sonar altitude can be obtained by using bottom tracking methods, but traditional methods often require manual thresholds or complex post-processing procedures, which cannot ensure accurate and real-time bottom tracking. In this paper, a real-time bottom tracking method of side scan data is proposed based on a one-dimensional convolution neural network. First, according to the characteristics of side scan backscatter strength sequences, positive (bottom sequences) and negative (water column and seabed sequences) samples are extracted to establish the sample sets. Second, a one-dimensional convolution neural network is carefully designed and trained by using the sample set to recognize the bottom sequences. Third, a complete processing procedure of the real-time bottom tracking method is established by traversing each side scan ping data and recognizing the bottom sequences. The auxiliary methods for improving real-time performance and sample data augmentation are also explained in detail. The proposed method is implemented on the measured side scan data from the marine area in Meizhou Bay. The trained network model achieves a 100% recognition of the initial sample set as well as 100% bottom tracking accuracy of the training survey line. The average bottom tracking accuracy of the testing survey lines excluding missed pings reaches 99.2%. By comparison with multi-beam bathymetric data and the statistical analysis of real-time performance, the experimental results prove the validity and accuracy of the proposed real-time bottom tracking method.height of the sonar from the seabed cannot be accurately obtained [7]. Inaccurate sonar heights will lead to inaccurate geocoding sonar images [8], confuse the water column information with the seabed information, and cause serious problems in applications of target recognition and segmentation [9][10][11], image interpretation [12,13], and seabed sediment classification [14][15][16]. The bottom tracking of side scan data can accurately obtain the sonar height from the seabed by finding the first echo that reaches the seabed. Meanwhile, real-time bottom tracking can quickly detect changes in sonar height and seabed terrain, and enhance the safety of sonar equipment and ship navigation.Side scan sonars can be installed on the vessel or towed close to the seabed from the survey ship. These sonars acquire high-resolution images by emitting sound pulses and recording the backscatter strengths from the water column and seabed [17]. The depth of the side scan sonar can be determined by the depth sensor, whereas the sonar height cannot be easily determined [18]. The sound wave transmits through the water column, and then arr...

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A Framework for Acoustic Segmentation Using Order Statistic-Constant False Alarm Rate in Two Dimensions From Sidescan Sonar Data

Cited by 16 publications

References 25 publications

Efficient approach for OS‐CFAR 2D technique using distributive histograms and breakdown point optimal concept applied to acoustic images

Efficient approach for OS‐CFAR 2D technique using distributive histograms and breakdown point optimal concept applied to acoustic images

MACÁBOT: Prototipo de Vehículo Autónomo de Superficie (ASV)

Real-Time Bottom Tracking Using Side Scan Sonar Data Through One-Dimensional Convolutional Neural Networks

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