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Crosstalk Removal in Forward Scan Sonar Image Using Deep Learning for Object Detection

Title
Crosstalk Removal in Forward Scan Sonar Image Using Deep Learning for Object Detection
Authors
MINSUNG, SUNG조현우KIM, TAE SIKJOE, HAN GILYU, SON CHEOL
Date Issued
Nov-2019
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Abstract
This paper proposes the detection and removal of crosstalk noise using a convolutional neural network in the images of forward scan sonar. Because crosstalk noise occurs near an underwater object and distorts the shape of the object, underwater object detection is limited. The proposed method can detect crosstalk noise using the neural network and remove crosstalk noise based on the detection result. Thus, the proposed method can be applied to other sonar-image-based algorithms and enhance the reliability of those algorithms. We applied the proposed method to a three-dimensional point cloud generation and generated a more accurate point cloud. We verified the performance of the proposed method by performing multiple indoor and field experiments.
Keywords
Crosstalk; Image enhancement; Neural networks; Object detection; Object recognition; Sonar; Underwater acoustics; Convolutional neural network; Crosstalk noise; Field experiment; Sonar image; Three-dimensional point clouds; Underwater object detection; Underwater objects; Underwater sonars; Deep learning
URI
http://oasis.postech.ac.kr/handle/2014.oak/99857
ISSN
1530-437X
Article Type
Article
Citation
IEEE SENSORS JOURNAL, vol. 19, no. 21, page. 9929 - 9944, 2019-11
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 YU, SON CHEOL
Dept. of Creative IT Engin.
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