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Side-scan sonar images segmentation for AUV with recurrent residual convolutional neural network module and self-guidance module

Yu, Fei / He, Bo / Li, Kaige / Yan, Tianhong / Shen, Yue / Wang, Qi / Wu, Meihan

Abstract This paper designed an end-to-end image segmentation method for side-scan sonar mounted on Autonomous Underwater Vehicle (AUV) to obtain accurate results. Accurate segmentation results depend on precise feature extraction, and an effective segmentation network can guarantee the autonomous recognition ability of Autonomous Underwater Vehicle (AUV) when performing tasks. In this paper, the R $^{2}$ CNN module is used to obtain accurate sonar image features, which reduces errors and improves accuracy. Besides, the Self-guidance module was introduced to ensure the network’s stability and optimize the segmentation results. The experimental results show that the proposed method can achieve better segmentation results than UNet, SegNet and their derivative networks and has better generalization ability.

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Side-scan sonar images segmentation for AUV with recurrent residual convolutional neural network module and self-guidance module

Yu, Fei / He, Bo / Li, Kaige / Yan, Tianhong / Shen, Yue / Wang, Qi / Wu, Meihan

Abstract This paper designed an end-to-end image segmentation method for side-scan sonar mounted on Autonomous Underwater Vehicle (AUV) to obtain accurate results. Accurate segmentation results depend on precise feature extraction, and an effective segmentation network can guarantee the autonomous recognition ability of Autonomous Underwater Vehicle (AUV) when performing tasks. In this paper, the R $^{2}$ CNN module is used to obtain accurate sonar image features, which reduces errors and improves accuracy. Besides, the Self-guidance module was introduced to ensure the network’s stability and optimize the segmentation results. The experimental results show that the proposed method can achieve better segmentation results than UNet, SegNet and their derivative networks and has better generalization ability.

Side-scan sonar images segmentation for AUV with recurrent residual convolutional neural network module and self-guidance module

Yu, Fei / He, Bo / Li, Kaige / Yan, Tianhong / Shen, Yue / Wang, Qi / Wu, Meihan

Abstract This paper designed an end-to-end image segmentation method for side-scan sonar mounted on Autonomous Underwater Vehicle (AUV) to obtain accurate results. Accurate segmentation results depend on precise feature extraction, and an effective segmentation network can guarantee the autonomous recognition ability of Autonomous Underwater Vehicle (AUV) when performing tasks. In this paper, the R $^{2}$ CNN module is used to obtain accurate sonar image features, which reduces errors and improves accuracy. Besides, the Self-guidance module was introduced to ensure the network’s stability and optimize the segmentation results. The experimental results show that the proposed method can achieve better segmentation results than UNet, SegNet and their derivative networks and has better generalization ability.

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Title:

Side-scan sonar images segmentation for AUV with recurrent residual convolutional neural network module and self-guidance module

Contributors:

Yu, Fei (author) / He, Bo (author) / Li, Kaige (author) / Yan, Tianhong (author) / Shen, Yue (author) / Wang, Qi (author) / Wu, Meihan (author)

Published in:

Applied Ocean Research ; 113

Publication date:

2021-02-25

ISSN:

DOI:

https://doi.org/10.1016/j.apor.2021.102608

Type of media:

Article (Journal)

Type of material:

Electronic Resource

Language:

English

Keywords:

Autonomous underwater vehicle (AUV) , Side-scan sonar (SSS) images , Semantic segmentation , Recurrent residual convolutional neural Network (R<math xmlns="http://www.w3.org/1998/Math/MathML"><msup><mrow></mrow> <mn>2</mn></msup></math>CNN) module , Self-guidance module

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