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

Qi Wang1, Meihan Wu2, Fei Yu3

  • 1School of Information Science and Engineering, Ocean University of China, Qingdao 266000, China. 18764819653@163.com.

Sensors (Basel, Switzerland)
|May 1, 2019
PubMed
Summary

We developed RT-Seg, a real-time semantic segmentation network for autonomous underwater vehicles (AUVs) processing high-resolution Side-Scan Sonar (SSS) images. This efficient network achieves excellent segmentation results, enabling smarter marine exploration.

Keywords:
depth-wise separable convolutionpatch-wise strategyreal-time semantic segmentationside-scan sonar (SSS)

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Area of Science:

  • Robotics and Autonomous Systems
  • Computer Vision
  • Marine Technology

Background:

  • Real-time processing of high-resolution sonar images is crucial for autonomous underwater vehicle (AUV) navigation and operation in complex marine environments.
  • Semantic segmentation of Side-Scan Sonar (SSS) images aids in target identification and environmental understanding.

Purpose of the Study:

  • To propose a novel real-time semantic segmentation network, RT-Seg, specifically designed for high-resolution SSS images.
  • To enhance the autonomy and intelligence of AUVs through efficient image processing.

Main Methods:

  • Developed a novel encoder-decoder architecture for RT-Seg, incorporating Depth-Wise Separable Convolution and a 2-way branch in the encoder.
  • Implemented a patch-wise strategy for training the network on high-resolution images.
  • Utilized an onboard Graphics Processing Unit (GPU) for real-time testing.

Main Results:

  • RT-Seg significantly reduces parameters and floating-point operations compared to existing networks.
  • Achieved a processing speed of 25.67 frames per second on a 500*500 input using an NVIDIA Jetson AGX Xavier.
  • Demonstrated excellent semantic segmentation accuracy on high-resolution SSS images.

Conclusions:

  • RT-Seg offers an efficient and effective solution for real-time semantic segmentation of SSS images.
  • The proposed network contributes to improving AUV capabilities in complex underwater environments.
  • The study provides insights into the speed-accuracy trade-offs for onboard SSS image processing.