A novel high precision mosaic method for sonar video sequence

Tang, Zhijie; Luo, Zhihang; Jiang, Lizhou; Ma, Gaoqian

doi:10.1007/s11042-020-10433-3

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…This method made the sonar image matching performance better, especially in special underwater environments. Finally, in the actual seabed acoustic image application, the fused data matching accuracy exceeds 70% [116]. To accurately realize the tracking effect of seafloor pipeline sutures, Wang et al combined spatial information and gradient information, and used the KAZE algorithm to segment the image into multiple regions [117].…”

Section: Other Technical Applicationsmentioning

confidence: 99%

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: Other Technical Applicationsmentioning

confidence: 99%

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

Shi,

Cao,

et al. 2024

Meas. Sci. Technol.

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“…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%

“…This highlights the necessity for improved historical attitude estimation. Some studies solely focus on rectifying position errors without addressing heading errors [11,14,15], thus limiting the accuracy of navigation.…”

Section: Introductionmentioning

confidence: 99%

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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