2026-04-29T01:00:05Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/229792024-11-29T03:03:43Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16967

00925njm 22002777a 4500 dc Burguera, Antoni author Bonin-Font, Francisco author 2020-08 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. 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 Sonar Underwater robotics Acoustic image segmentation Neural network On-Line Multi-Class Segmentation of Side-Scan Sonar Imagery Using an Autonomous Underwater Vehicle