Automatic Target Recognition for an Autonomous Underwater Vehicle (auv) Sidescan Sonar in Complex Environments
نویسنده
چکیده
In this paper the automated detection and classification of mine-like objects in sidescan (Marine Sonics) data is investigated. In particular, 4 different sites are considered involving sidescan sonar data collected by Australian, New Zealand, and NATO Undersea Research Centre (NURC) REMUS AUVs. Twelve different automated detection methods are proposed and their individual and fused performances are investigated. The different data sets contain different types of seabeds including relatively featureless, very-rippled, pock-marked, and cluttered seabeds. The performances of the detectors are also considered with respect to the different sites. After a detection/fusion process, the extracted small sonar images (mugshots) are further classified using template-matching. Examples of the template-matching are given.
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