Study on the Classification Performance of Underwater Sonar Image Classification Based on Convolutional Neural Networks for Detecting a Submerged Human Body
Open accessAbstract
Auto-detecting a submerged human body underwater is very challenging with the absolute necessity to a diver or a submersible. For the vision sensor, the water turbidity and limited light condition make it difficult to take clear images. For this reason, sonar sensors are mainly utilized in water. However, even though a sonar sensor can give a plausible underwater image within this limitation, the sonar image’s quality varies greatly depending on the background of the target. The readability of the sonar image is very different according to the target distance from the underwater floor or the incidence angle of the sonar sensor to the floor. The target background must be very considerable because it causes scattered and polarization noise in the sonar image. To successfully classify the sonar image with these noises, we adopted a Convolutional Neural Network (CNN) such as AlexNet and GoogleNet. In preparing the training data for this model, the data augmentation on scattering and polarization were implemented to improve the classification accuracy from the original sonar image. It could be practical to classify sonar images undersea even by training sonar images only from the simple testbed experiments. Experimental validation was performed using three different datasets of underwater sonar images from a submerged body of a dummy, resulting in a final average classification accuracy of 91.6% using GoogleNet.
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Testing with real-world images revealed poor performance of AlexNet overall, whereas GoogLeNet obtained an accuracy of 91.6% when trained with input data with various noise levels.
“The accuracy obtained testing the CNN with the real-world set of images revealed a poor performance of AlexNet overall, whereas GoogLeNet obtained an accuracy of 91.6% when trained with input data with various noise levels.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [130]
Use of augmented data in the training stage generalised well to real-world problems, providing a classification accuracy of 91.6% using GoogLeNet as the classifier.
“The use of augmented data in the training stage proved to generalise well to the real-world problems, providing a classification accuracy of 91.6% using GoogLeNet as the classifier.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [130]
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- [1]Francisco F. Jan S. Detection of Visual Signatures of Marine Mammals and Fish within Marine Renewable Energy Farms using Multibeam Imaging Sonar J. Mar. Sci. Eng. 2019 7 22 10.3390/jmse7020022 · DOI
- [2]Lee S. Deep Learning of Submerged Body Images from 2D Sonar Sensor based on Convolutional Neural Network Proceedings of the 2017 IEEE Underwater Technology (UT) Busan, Korea 21–24 February 2017
- [4]Cho H. Gu J. Joe H. Asada A. Yu S.C. Acoustic beam profile-based rapid underwater object detection for an imaging sonar J. Mar. Sci. Technol. 2015 20 180 197 10.1007/s00773-014-0294-x · DOI
- [5]Purcell M. Gallo D. Packard G. Dennett M. Rothenbeck M. Sherrell A. Pascaud S. Use of REMUS 6000 AUVs in the search for the Air France Flight 447 Proceedings of the OCEANS’11 MTS/IEEE KONA Waikoloa, HI, USA 19–22 September 2011 1 7
- [6]Reed S. Petillot Y. Bell J. An automatic approach to the detection and extraction of mine features in sidescan sonar IEEE J. Ocean. Eng. 2003 28 90 105 10.1109/JOE.2002.808199 · DOI
- [7]Williams D.P. Groen J. A fast physics-based, environmentally adaptive underwater object detection algorithm Proceedings of the OCEANS Santander, Spain 6–9 June 2011 1 7
- [8]Galceran E. Djapic V. Carreras M. Williams D.P. A real-time underwater object detection algorithm for multi-beam forward looking sonar IFAC Proc. Vol. 2012 45 306 311 10.3182/20120410-3-PT-4028.00051 · DOI
- [9]Belcher E.O. Lynn D.C. Acoustic near-video-quality images for work in turbid water Proceedings of the Underwater Intervention Houston, TX, USA 24–26 January 2000
- [10]Lee Y. Kim T.G. Choi H.T. Preliminary study on a framework for imaging sonar based underwater object recognition Proceedings of the 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI) Jeju, Korea 30 October–2 November 2013 517 520
- [11]Ancuti C.O. Ancuti C. De Vleeschouwer C. Bekaert P. Color Balance and Fusion for Underwater Image Enhancement IEEE Trans. Image Process. 2018 27 379 393 10.1109/TIP.2017.2759252 28981416 · DOI · Paper page
- [12]LeCun Y. Bengio Y. Hinton G.E. Deep learning Nature 2015 521 436 444 10.1038/nature14539 26017442 · DOI · Paper page
- [14]Szegedy C. Liu W. Jia Y. Sermanet P. Reed S. Anguelov D. Erhan D. Vanhoucke V. Rabinovich A. Going Deeper with Convolutions Proceedings of the CVPR Boston, MA, USA 7–12 June 2015 1 9
- [15]Lee S. Park B. Kim A. A Deep Learning based Submerged Body Classification Using Underwater Imaging Sonar Proceedings of the 16th International Conference on Ubiquitous Robots Jeju, Korea 24–27 June 2019 Volume 20 106 112
- [16]Cadieu C.F. Hong H. Yamins D.L.K. Pinto N. Ardila D. Solomon E.A. Majaj N.J. DiCarlo J.J. Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition PLoS Comput. Biol. 2014 10 1 18 10.1371/journal.pcbi.1003963 25521294 PMC4270441 · DOI · Paper page
- Authors
- Nguyen, H.-T., Lee, E.-H., & Lee, S.
- Affiliations
- Kongju National University (KR)
- Corresponding author
- Lee, Sejin
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2019-12-23
- Volume
- 20
- Issue
- 1
- Pages
- 94
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC6982957
- OpenAlex
- W2996325784
- Cited by
- 71
- References
- 16
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 20 · 2023: 10 · 2022: 9 · 2021: 12 · 2020: 8
- FWCI
- 4.00
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Kongju National University (2019-0161-01)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Underwater Acoustics Research
- Physical Sciences › Engineering › Ocean Engineering › Underwater Vehicles and Communication Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- sonar image classification, convolutional neural networks, GoogLeNet, AlexNet, polarization noise, data augmentation
- MeSH terms
- Ultrasonic Waves, Humans, Image Processing, Computer-Assisted, Neural Networks, Computer, Human Body
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