A Comparison of Feature Detectors for Underwater Sonar Imagery

Tueller, Peter; Kastner, Ryan; Diamant, Roee

doi:10.1109/oceans.2018.8604786

Cited by 12 publications

(11 citation statements)

References 9 publications

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“…This could be paired with additional research into a more robust sonar image simulator. The one used in this paper has limited dimensionality, and as such generating additional images to use a neural network runs the risk of overfitting [29].…”

Section: Discussionmentioning

confidence: 99%

Target detection using features for sonar images

Tueller

Kastner

Diamant³

2020

IET Radar, Sonar & Navigation

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Section: Discussionmentioning

confidence: 99%

Target detection using features for sonar images

Tueller

Kastner

Diamant³

2020

IET Radar, Sonar & Navigation

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“…In [21], Barngrover et al also utilized the Haar-like feature classifier to generate image patches (around regions of interest), which are then processed by subjects using the rapid serial visual presentation paradigm. Other feature-based methods used the geometric visual descriptors, such as scale-invariant feature transform (SIFT) [18], [32], [33] and local binary pattern (LBP) [20], [34]. In [18], Hollensen et al adopted the dense SIFT feature extraction with various window sizes for computing orientation histograms.…”

Section: B Traditional Mine-like Object Detection Methodsmentioning

confidence: 99%

Deep Gabor Neural Network for Automatic Detection of Mine-Like Objects in Sonar Imagery

Phung

Chapple

et al. 2020

IEEE Access

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With the advances in sonar imaging technology, sonar imagery has increasingly been used for oceanographic studies in civilian and military applications. High-resolution imaging sonars can be mounted on various survey platforms, typically autonomous underwater vehicles, which provide enhanced speed and improved data quality with long-range support. This paper addresses the automatic detection of mine-like objects using sonar images. The proposed Gabor-based detector is designed as a feature pyramid network with a small number of trainable weights. Our approach combines both semantically weak and strong features to handle mine-like objects at multiple scales effectively. For feature extraction, we introduce a parameterized Gabor layer which improves the generalization capability and computational efficiency. The steerable Gabor filtering modules are embedded within the cascaded layers to enhance the scale and orientation decomposition of images. The entire deep Gabor neural network is trained in an end-to-end manner from input sonar images with annotated mine-like objects. An extensive experimental evaluation on a real sonar dataset shows that the proposed method achieves competitive performance compared to the existing approaches.

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“…A few works [2,3] have applied and compared the application of feature extraction algorithms for acoustic image analysis but the conclusions diverge, especially if their performances on optical images [5,6,8] are taken into account. As reported in the literature, for optical images, ORB (Oriented FAST and Rotated BRIEF) and BRISK (Binary Robust Invariant Scalable Keypoints) are better in repeatability and computational efficiency than SURF (Speeded-Up Robust Features) or SIFT (Scale Invariant Feature Transform) [5].…”

Section: Algorithm Selectionmentioning

confidence: 99%

“…The characteristics of such environments and the lack of previous knowledge about its layout accentuate the need for accurate localization. In recent years there has been increased research interest on the application of these algorithms towards acoustic image analysis [2][3][4], due to the recorded performance for optical imaging [5][6][7][8]. Furthermore, for localization purposes, the goal of employing these algorithms is not to identify particular sets of features, unlike more specific solutions proposed in the literature [9], but rather to achieve a generic solution, enabling its application in unknown environments.…”

Section: Introductionmentioning

confidence: 99%

“…Such is evident in the acoustic representation of a square water tank, shown in Figure 2. Secondly, acoustic images are affected by the presence of acoustic shadows, low resolution, distortion, and range-varying attenuation [3]. In more confined areas, the occurrence of multiple echoes makes it even harder to extract useful information for navigation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Performance Analysis of Feature Extraction Algorithms for Acoustic Image-Based Underwater Navigation

Oliveira

Ferreira

Cruz

2021

JMSE

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In underwater navigation, sonars are useful sensing devices for operation in confined or structured environments, enabling the detection and identification of underwater environmental features through the acquisition of acoustic images. Nonetheless, in these environments, several problems affect their performance, such as background noise and multiple secondary echoes. In recent years, research has been conducted regarding the application of feature extraction algorithms to underwater acoustic images, with the purpose of achieving a robust solution for the detection and matching of environmental features. However, since these algorithms were originally developed for optical image analysis, conclusions in the literature diverge regarding their suitability to acoustic imaging. This article presents a detailed comparison between the SURF (Speeded-Up Robust Features), ORB (Oriented FAST and Rotated BRIEF), BRISK (Binary Robust Invariant Scalable Keypoints), and SURF-Harris algorithms, based on the performance of their feature detection and description procedures, when applied to acoustic data collected by an autonomous underwater vehicle. Several characteristics of the studied algorithms were taken into account, such as feature point distribution, feature detection accuracy, and feature description robustness. A possible adaptation of feature extraction procedures to acoustic imaging is further explored through the implementation of a feature selection module. The performed comparison has also provided evidence that further development of the current feature description methodologies might be required for underwater acoustic image analysis.

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A Comparison of Feature Detectors for Underwater Sonar Imagery

Cited by 12 publications

References 9 publications

Target detection using features for sonar images

Target detection using features for sonar images

Deep Gabor Neural Network for Automatic Detection of Mine-Like Objects in Sonar Imagery

A Performance Analysis of Feature Extraction Algorithms for Acoustic Image-Based Underwater Navigation

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