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Probability-Based Recognition Framework for Underwater Landmarks Using Sonar Images

Marine Robotics Laboratory, Korea Research Institute of Ships and Ocean Engineering, Daejeon 34103, Korea
Department of Electronics Engineering, Chosun University, Gwangju 61452, Korea
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in 10th International Conference on Ubiquitous Robots and Ambient Intelligence, Jeju, Korea, 30 October–2 November 2013.
Sensors 2017, 17(9), 1953;
Received: 5 July 2017 / Revised: 18 August 2017 / Accepted: 21 August 2017 / Published: 24 August 2017
(This article belongs to the Special Issue Advances and Challenges in Underwater Sensor Networks)
PDF [7740 KB, uploaded 24 August 2017]


This paper proposes a probability-based framework for recognizing underwater landmarks using sonar images. Current recognition methods use a single image, which does not provide reliable results because of weaknesses of the sonar image such as unstable acoustic source, many speckle noises, low resolution images, single channel image, and so on. However, using consecutive sonar images, if the status—i.e., the existence and identity (or name)—of an object is continuously evaluated by a stochastic method, the result of the recognition method is available for calculating the uncertainty, and it is more suitable for various applications. Our proposed framework consists of three steps: (1) candidate selection, (2) continuity evaluation, and (3) Bayesian feature estimation. Two probability methods—particle filtering and Bayesian feature estimation—are used to repeatedly estimate the continuity and feature of objects in consecutive images. Thus, the status of the object is repeatedly predicted and updated by a stochastic method. Furthermore, we develop an artificial landmark to increase detectability by an imaging sonar, which we apply to the characteristics of acoustic waves, such as instability and reflection depending on the roughness of the reflector surface. The proposed method is verified by conducting basin experiments, and the results are presented. View Full-Text
Keywords: underwater object recognition; framework; artificial landmark; imaging sonar; robot intelligence underwater object recognition; framework; artificial landmark; imaging sonar; robot intelligence

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Lee, Y.; Choi, J.; Ko, N.Y.; Choi, H.-T. Probability-Based Recognition Framework for Underwater Landmarks Using Sonar Images . Sensors 2017, 17, 1953.

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