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Robust 3D Shape Classification Method using Simulated Multi View Sonar Images and Convolutional Nueral Network

Title
Robust 3D Shape Classification Method using Simulated Multi View Sonar Images and Convolutional Nueral Network
Authors
YU, SON CHEOLJASON, KIMMEUNGSUK, LEE
Date Issued
19-Jun-2019
Publisher
Oceans 2019 MTS/IEEE
Abstract
Object detection and classification in the water enhances not only the application of the autonomous underwater vehicle(AUV) but also localization of the AUV. Object detection and classification using sonar images are challenging problems due to low resolution and low signal-to-noise ratio. In this paper, we propose shape classification method using multi-view sonar images for AUV. To train multi-view of sonar images, we used network which is connected in parallel with convolutional neural network(CNN). We used Alex-net for the basic CNN model. The extracted features by the CNN are collected through the pooling layer and connected to the fully connected layer to classify the shape. To overcome the lack of training data, sonar simulator was used to generate data set. As a result, 6 shape are well classified and also shows possibility for the recognition of the real sonar images acquired in water tank.
URI
http://oasis.postech.ac.kr/handle/2014.oak/99520
ISSN
978-1-728
Article Type
Conference
Citation
Oceans 2019 MTS/IEEE Marseille-France, page. 1 - 5, 2019-06-19
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 YU, SON CHEOL
Dept. of Creative IT Engin.
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