A SIFT‐Like Feature Detector and Descriptor for Multibeam Sonar Imaging

Zhang, Wanyuan; Zhou, Tian; Xu, Chao; Liu, Meiqin

doi:10.1155/2021/8845814

Cited by 8 publications

(5 citation statements)

References 28 publications

(37 reference statements)

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“…The experimental data verify that this method can more accurately obtain the characteristics of the seabed oil and gas pipeline structure [81,85]. The morphological simulation of oil and gas pipeline leakage objects can also be reconstructed in 3D [86]. This engineering method can be applied to the detection of oil and gas leaks in subsea pipelines well.…”

Section: Applicationsmentioning

confidence: 69%

Sonar image intelligent processing in seabed pipeline detection: review and application

Shi,

Cao,

et al. 2024

Meas. Sci. Technol.

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Subsea pipelines rely primarily on imaging sonar for detection and identification. We analyze the imaging principles of side scan sonar, multi-beam sonar, synthetic aperture sonar, seafloor penetrating sonar and forward-looking sonar. We discuss their effectiveness in detecting seabed pipelines, as well as their limitations in image recognition capabilities. As intelligent algorithms have become increasingly important in the field of image processing, we review the sonar image intelligent detection and recognition algorithms in the past six years and summarize the internal principles and application effects of classic algorithms such as SIFT (Scale-Invariant Feature Transform), KMA (K-means algorithm), and CFAR (Constant False-Alarm Rate) that currently show good application prospects. Simultaneously, we review the particular strengths exhibited by these algorithms, such as contour feature extraction, image segmentation and clustering, target recognition under background noise, etc. The research on intelligent processing of sonar images opens up a new way to solve the difficult problem of the seabed targets detection and recognition.

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Section: Applicationsmentioning

confidence: 69%

Sonar image intelligent processing in seabed pipeline detection: review and application

Shi,

Cao,

et al. 2024

Meas. Sci. Technol.

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“…The existing velocity pre-integration for FGO is expressed as (12), ∆v k which is the integral of the relative force in the b frame.…”

Section: Velocity Updating and Velocity Pre-integrationmentioning

confidence: 99%

“…Both the contribution of Earth's rotation C n(k) n(i) and the compensation for paddle errors ∆v b(k−1) scul(k) are considered to enhance performance. The consideration of the Earth's rotation contributes to the heading convergence through the Earth's rotation, which is not achievable with the traditional method (12).…”

Section: Velocity Updating and Velocity Pre-integrationmentioning

confidence: 99%

“…The first challenge pertains to the feature-based nature of existing SSS image registration methods, which are prone to failure due to acoustic noise. The existing acoustic image-aided navigation uses feature-based registration methods, which require complex terrains to provide feature points [11,12]. However, the seabed generally lacks complex terrains, which often results in the easy failure of feature-based registration methods.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing AUV Navigation Using Factor Graphs with Side-Scan Sonar Integration

Zhang,

Gao,

Guan

2024

JMSE

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For seabed mapping, the prevalence of autonomous underwater vehicles (AUVs) employing side-scan sonar (SSS) necessitates robust navigation solutions. However, the positioning errors of traditional strapdown inertial navigation system (SINS) and Doppler velocity log (DVL) systems accumulated significantly, further exacerbated by DVL’s susceptibility to failure in complex underwater conditions. This research proposes an integrated navigation approach that utilizes factor graph optimization (FGO) along with an improved pre-integration technique integrating SSS-derived position measurements. Firstly, the reliability of SSS image registration in the presence of strong noise and feature-poor environments is improved by replacing the feature-based methods with a Fourier-based method. Moreover, the high-precision inertial measurement unit (IMU) pre-integration method could correct the heading errors of SINS significantly by considering the Earth’s rotation. Finally, the AUV’s marine experimental results demonstrated that the proposed integration method not only offers improved SSS image registration and corrects initial heading discrepancies but also delivers greater system stability, particularly in instances of DVL data loss.

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“…However, when there are apparent noise interference and image distortion in the sonar image, the matching effects of SIFT will also be significantly reduced. Zhang [10] proposed a SIFT-like feature detector and descriptor MBS-SIFT that could robustly cope with noise interference in multibeam imaging sonar images, which could more accurately capture the features of underwater objects compared with the classical SIFT method. Tao [11] proposed a combined SSS image-matching method in a priori position information, and SURF detectors are used to detect feature points from the SSS image that are stable to the affine transform for matching, and finally, the results are corrected with the help of RANSAC (random sample consensus).…”

Section: Related Workmentioning

confidence: 99%

Performance Comparison of Feature Detectors on Various Layers of Underwater Acoustic Imagery

Zhou

Yuan

et al. 2022

JMSE

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Image feature matching is essential in many computer vision applications, and the foundation of matching is feature detection, which is a crucial feature quantification process. This manuscript focused on detecting more features from underwater acoustic imageries for further ocean engineering applications of autonomous underwater vehicles (AUVs). Currently, the mainstream feature detection operators are developed for optical images, and there is not yet a feature detector oriented to underwater acoustic imagery. To better analyze the suitability of existing feature detectors for acoustic imagery and develop an operator that can robustly detect feature points in underwater imageries in the future, this manuscript compared the performance of well-established handcrafted feature detectors and that of the increasingly popular deep-learning-based detectors to fill the gap in the literature. The datasets tested are from the most commonly used side-scan sonars (SSSs) and forward-looking sonars (FLSs). Additionally, the detection idea of these detectors on the acoustic imagery phase congruency (PC) layer was innovatively proposed with the aim of finding a solution that balances detection accuracy and speed. The experimental results show that the ORB (Oriented FAST and Rotated BRIEF) and BRISK (Binary Robust Invariant Scalable Keypoints) detectors achieve the best overall performance, the FAST detector is the fastest, and the PC and Sobel layers are the most favorable for implementing feature detection.

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A SIFT‐Like Feature Detector and Descriptor for Multibeam Sonar Imaging

Cited by 8 publications

References 28 publications

Sonar image intelligent processing in seabed pipeline detection: review and application

Sonar image intelligent processing in seabed pipeline detection: review and application

Optimizing AUV Navigation Using Factor Graphs with Side-Scan Sonar Integration

Performance Comparison of Feature Detectors on Various Layers of Underwater Acoustic Imagery

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