Side-scan Sonar Image Rough Recognition and Feature Matching Based on CNN and SIFT

Dong, Chaoping; Guo, Longxiang; Hu, Kejia; Yin, Jingwei; Sheng, Xueli

doi:10.1109/icsidp47821.2019.9172987

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…In the field of high-performance registration of sonar images, Dong et al used the joint algorithm of LeNet-5 and SIFT to enhance the recognition of sonar image data [82]. It solves the problems of poor robustness and low resolution of sonar image detection [83,84].…”

Section: 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: Applicationsmentioning

confidence: 99%