Rapid shift of surface sedimentary facies and its depositional mechanism in the macrotidal wave-dominated Sinduri Bay, west coast of Korea

Yoon, HyunHo; Chun, Sejong

doi:10.14770/jgsk.2019.55.3.257

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…In the case of the Yellow Sea, the composition of sand and mud sediments of land origin is mainly influenced by the YSWC (Yellow Sea Warm Current) and JCC (Jiangsu Coastal Current). Moreover, the characteristics of the southeastern part of the Yellow Sea vary depending on the silt-to-clay ratio [18][19][20][21][22]. The Southern Sea differs depending on the sea area, but the sediments vary widely from silty to sandy surfaces [23].…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Underwater Object Detection off the Coast of the Korean Peninsula

Kim

Bae

Son

et al. 2022

JMSE

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Recently, neural network-based deep learning techniques have been actively applied to detect underwater objects in sonar (sound navigation and ranging) images. However, unlike optical images, acquiring sonar images is extremely time- and cost-intensive, and therefore securing sonar data and conducting related research can be rather challenging. Here, a side-scan sonar was used to obtain sonar images to detect underwater objects off the coast of the Korean Peninsula. For the detection experiments, we used an underwater mock-up model with a similar size, shape, material, and acoustic characteristics to the target object that we wished to detect. We acquired various side-scan sonar images of the mock-up object against the background of mud, sand, and rock to account for the different characteristics of the coastal and seafloor environments of the Korean Peninsula. To construct a detection network suitable for the obtained sonar images from the experiment, the performance of five types of feature extraction networks and two types of optimizers was analyzed. From the analysis results, it was confirmed that performance was achieved when DarkNet-19 was used as the feature extraction network, and ADAM was applied as the optimizer. However, it is possible that there are feature extraction network and optimizer that are more suitable for our sonar images. Therefore, further research is needed. In addition, it is expected that the performance of the modified detection network can be more improved if additional images are obtained.

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