Paper Info
Title
Probability-Based Recognition Framework for Underwater Landmarks Using Sonar Images †.
Abstract
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.
Year
DOI
Venue
2017
10.3390/s17091953
SENSORS
Keywords
Field
DocType
underwater object recognition,framework,artificial landmark,imaging sonar,robot intelligence
Computer vision,Speckle pattern,Communication channel,Filter (signal processing),Sonar,Artificial intelligence,Engineering,Landmark,Acoustic wave,Bayesian probability,Underwater
Journal
Volume
Issue
Citations 
17
9
0
PageRank 
References 
Authors
0.34
6
4
Name
Order
Citations
PageRank
Yeongjun Lee103.04
Jinwoo Choi202.37
Nak Yong Ko33712.59
Hyun-Taek Choi4147.98