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This paper details a new method to recognize and detect underwater objects inreal-time sidescan sonar data imagery streams, with case-studies ofapplications for underwater archeology, and ghost fishing gear retrieval. Wefirst synthesize images from sidescan data, apply geometric and radiometriccorrections, then use 2D feature detection algorithms to identify point cloudsof descriptive visual microfeatures such as corners and edges in the sonarimages. We then apply a clustering algorithm on the feature point clouds togroup feature sets into regions of interest, reject false positives, yielding ageoreferenced inventory of objects.

A machine vision meta-algorithm for automated recognition of underwater objects using sidescan sonar imagery