2026-04-29T04:08:29Zhttps://repisalud.isciii.es/rest/oai/requestoai:repisalud.isciii.es:20.500.12105/229792024-11-29T03:03:43Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16967
Repisalud
author
Burguera, Antoni
author
Bonin-Font, Francisco
2024-09-13T09:14:47Z
2024-09-13T09:14:47Z
2020-08
Burguera A, Bonin-Font F. On-Line Multi-Class Segmentation of Side-Scan Sonar Imagery Using an Autonomous Underwater Vehicle. J Mar Sci Eng. 2020 Aug;8(8):557.
https://hdl.handle.net/20.500.13003/19728
https://hdl.handle.net/20.500.12105/22979
10.3390/jmse8080557
2077-1312
Journal of Marine Science and Engineering
2-s2.0-85089491265
568081500001
This paper proposes a method to perform on-line multi-class segmentation of Side-Scan Sonar acoustic images, thus being able to build a semantic map of the sea bottom usable to search loop candidates in a SLAM context. The proposal follows three main steps. First, the sonar data is pre-processed by means of acoustics based models. Second, the data is segmented thanks to a lightweight Convolutional Neural Network which is fed with acoustic swaths gathered within a temporal window. Third, the segmented swaths are fused into a consistent segmented image. The experiments, performed with real data gathered in coastal areas of Mallorca (Spain), explore all the possible configurations and show the validity of our proposal both in terms of segmentation quality, with per-class precisions and recalls surpassing the 90%, and in terms of computational speed, requiring less than a 7% of CPU time on a standard laptop computer. The fully documented source code, and some trained models and datasets are provided as part of this study.
eng
Sonar
Underwater robotics
Acoustic image segmentation
Neural network
On-Line Multi-Class Segmentation of Side-Scan Sonar Imagery Using an Autonomous Underwater Vehicle
research article