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Pre-trained Models for Sonar Images

  • Machine learning and neural networks are now ubiquitous in sonar perception, but it lags behind the computer vision field due to the lack of data and pre-trained models specifically for sonar images. In this paper we present the Marine Debris Turntable dataset and produce pre-trained neural networks trained on this dataset, meant to fill the gap of missing pre-trained models for sonar images. We train Resnet 20, MobileNets, DenseNet121, SqueezeNet, MiniXception, and an Autoencoder, over several input image sizes, from 32 x 32 to 96 x 96, on the Marine Debris turntable dataset. We evaluate these models using transfer learning for low-shot classification in the Marine Debris Watertank and another dataset captured using a Gemini 720i sonar. Our results show that in both datasets the pre-trained models produce good features that allow good classification accuracy with low samples (10-30 samples per class). The Gemini dataset validates that the features transfer to other kinds of sonar sensors. We expect that the community benefits from the public release of our pre-trained models and the turntable dataset.

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Document Type:Preprint
Author:Matias Valdenegro-Toro, Alan Preciado-Grijalva, Bilal Wehbe
Number of pages:8
ArXiv Id:http://arxiv.org/abs/2108.01111
Date of first publication:2021/08/02
Publication status:Global Oceans 2021
Funding:This work has been partially supported by the H2020-ICT-2020-2 ICT-47- 2020 project DeeperSense (Ref 101016958) funded by the European Union’s Horizon 2020 research and innovation programme.
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2022/03/09
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International