Methods for Underwater Sonar Image Processing in Objection Detection

Tang, Xin-Yu; Zhang, Xuewu; Xu, Xiaolong; Su, Jinbao

doi:10.1109/iccsec.2017.8446701

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…As shown in figure 12, this section reviews the application of three key image intelligent processing detection in seafloor pipelines [77]. And we summarize the current accurate detection algorithm principle and engineering examples of pipelines [78]. Due to the complexity and randomness of ocean background noise, pipeline detection is a challenging and crucial issue.…”

Section: Seabed Pipeline Identification Based On Image Intelligent Pr...mentioning

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: Seabed Pipeline Identification Based On Image Intelligent Pr...mentioning

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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“…This method demonstrates significant advantages in improving the accuracy and real-time performance of underwater target detection. In another study, Xinyu et al (2017) [21] investigated a sonar image processing method that integrates k-means clustering-based image segmentation for denoising and segmenting sonar images. This approach effectively distinguishes targets, background, and noise within the images, providing an effective tool for sonar image analysis.…”

Section: Introductionmentioning

confidence: 99%

Side-Scan Sonar Image Matching Method Based on Topology Representation

Yang,

Yu,

Wang

et al. 2024

JMSE

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In the realm of underwater environment detection, achieving information matching stands as a pivotal step, forming an indispensable component for collaborative detection and research in areas such as distributed mapping. Nevertheless, the progress in studying the matching of underwater side-scan sonar images has been hindered by challenges including low image quality, intricate features, and susceptibility to distortion in commonly used side-scan sonar images. This article presents a comprehensive overview of the advancements in underwater sonar image processing. Building upon the novel SchemaNet image topological structure extraction model, we introduce a feature matching model grounded in side-scan sonar images. The proposed approach employs a semantic segmentation network as a teacher model to distill the DeiT model during training, extracting the attention matrix of intermediate layer outputs. This emulates SchemaNet’s transformation method, enabling the acquisition of high-dimensional topological structure features from the image. Subsequently, utilizing a real side-scan sonar dataset and augmenting data, we formulate a matching dataset and train the model using a graph neural network. The resulting model demonstrates effective performance in side-scan sonar image matching tasks. These research findings bear significance for underwater detection and target recognition and can offer valuable insights and references for image processing in diverse domains.

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“…Denoising methods based on principal component analysis (PCA) and singular value decomposition (SVD) have also been proposed [19], showing usefulness in preprocessing sonar images for underwater target tracking algorithms [20]. Other transform domain methods, including curve transform [21], [22], discrete cosine transform (DCT) [23], Radon transform [24], and shearlet transform [25], have been applied to sonar image denoising as well. Existing transform domain methods have not effectively utilized the multi-scale characteristics of the FLS image.…”

Section: Introductionmentioning

confidence: 99%

LPMsDE: Multi-Scale Denoising and Enhancement Method Based on Laplacian Pyramid Framework for Forward-Looking Sonar Image

Wang,

Li,

Teng

et al. 2023

IEEE Access

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Forward-looking sonar (FLS) images present various challenges in interpretation, recognition, and segmentation due to limitations like low resolution, speckle noise, and low contrast, making them more complex than optical images. Existing methods often focus solely on denoising or enhancement, neglecting the potential benefits of utilizing multi-scale features to create an integrated image processing approach. This paper introduces the Laplacian pyramid-based Multi-scale Denoising and Enhancement (LPMsDE) method tailored for FLS images. The proposed method begins by presenting a novel multiplicative speckle noise model, grounded in the Gaussian distribution, specifically designed for FLS images. Next, the Laplacian pyramid decomposition is utilized to estimate noise variance, with an improved adaptive local filter. Lastly, a combination of the Laplacian pyramid framework, the enhanced adaptive local filter, and Contrast-Limited Histogram Equalization (CLHE) is employed to denoise and enhance images at different resolution levels. Through comprehensive experiments conducted on both simulated and real sonar images, the effectiveness of the LPMsDE method is demonstrated. It surpasses other denoising and enhancement techniques, as evidenced by superior scores in Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), Equivalent Number of Looks (ENL), Natural Image Quality Evaluator (NIQE), and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE).

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Methods for Underwater Sonar Image Processing in Objection Detection

Cited by 7 publications

References 7 publications

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

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

Side-Scan Sonar Image Matching Method Based on Topology Representation

LPMsDE: Multi-Scale Denoising and Enhancement Method Based on Laplacian Pyramid Framework for Forward-Looking Sonar Image

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