RT-Seg: A Real-Time Semantic Segmentation Network for Side-Scan Sonar Images

Wang, Qi; Wu, Meihan; Yu, Fei; Feng, Chen; Li, Kaige; Zhu, Yuemei; Rigall, Eric; He, Bo

doi:10.3390/s19091985

Open AccessArticle

RT-Seg: A Real-Time Semantic Segmentation Network for Side-Scan Sonar Images

by

Qi Wang

,

Meihan Wu

,

Fei Yu

,

Chen Feng

^*,

Kaige Li

,

Yuemei Zhu

,

Eric Rigall

and

Bo He

School of Information Science and Engineering, Ocean University of China, Qingdao 266000, China

^*

Author to whom correspondence should be addressed.

Sensors 2019, 19(9), 1985; https://doi.org/10.3390/s19091985

Submission received: 1 April 2019 / Revised: 24 April 2019 / Accepted: 25 April 2019 / Published: 28 April 2019

(This article belongs to the Special Issue Sensors Signal Processing and Visual Computing 2019)

Download

Browse Figures

Review Reports Versions Notes

Abstract

Real-time processing of high-resolution sonar images is of great significance for the autonomy and intelligence of autonomous underwater vehicle (AUV) in complex marine environments. In this paper, we propose a real-time semantic segmentation network termed RT-Seg for Side-Scan Sonar (SSS) images. The proposed architecture is based on a novel encoder-decoder structure, in which the encoder blocks utilized Depth-Wise Separable Convolution and a 2-way branch for improving performance, and a corresponding decoder network is implemented to restore the details of the targets, followed by a pixel-wise classification layer. Moreover, we use patch-wise strategy for splitting the high-resolution image into local patches and applying them to network training. The well-trained model is used for testing high-resolution SSS images produced by sonar sensor in an onboard Graphic Processing Unit (GPU). The experimental results show that RT-Seg can greatly reduce the number of parameters and floating point operations compared to other networks. It runs at 25.67 frames per second on an NVIDIA Jetson AGX Xavier on 500*500 inputs with excellent segmentation result. Further insights on the speed and accuracy trade-off are discussed in this paper.

Keywords: side-scan sonar (SSS); real-time semantic segmentation; depth-wise separable convolution; patch-wise strategy

Share and Cite

MDPI and ACS Style

Wang, Q.; Wu, M.; Yu, F.; Feng, C.; Li, K.; Zhu, Y.; Rigall, E.; He, B. RT-Seg: A Real-Time Semantic Segmentation Network for Side-Scan Sonar Images. Sensors 2019, 19, 1985. https://doi.org/10.3390/s19091985

AMA Style

Wang Q, Wu M, Yu F, Feng C, Li K, Zhu Y, Rigall E, He B. RT-Seg: A Real-Time Semantic Segmentation Network for Side-Scan Sonar Images. Sensors. 2019; 19(9):1985. https://doi.org/10.3390/s19091985

Chicago/Turabian Style

Wang, Qi, Meihan Wu, Fei Yu, Chen Feng, Kaige Li, Yuemei Zhu, Eric Rigall, and Bo He. 2019. "RT-Seg: A Real-Time Semantic Segmentation Network for Side-Scan Sonar Images" Sensors 19, no. 9: 1985. https://doi.org/10.3390/s19091985

APA Style

Wang, Q., Wu, M., Yu, F., Feng, C., Li, K., Zhu, Y., Rigall, E., & He, B. (2019). RT-Seg: A Real-Time Semantic Segmentation Network for Side-Scan Sonar Images. Sensors, 19(9), 1985. https://doi.org/10.3390/s19091985

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

RT-Seg: A Real-Time Semantic Segmentation Network for Side-Scan Sonar Images

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI