A machine vision meta-algorithm for automated recognition of underwater objects using sidescan sonar imagery
This addresses the challenge of real-time object recognition in underwater sonar data for marine researchers and environmental cleanup efforts.
The paper tackles the problem of recognizing underwater objects in real-time sidescan sonar imagery, achieving automated detection with applications in underwater archaeology and ghost fishing gear retrieval.
This paper details a new method to recognize and detect underwater objects in real-time sidescan sonar data imagery streams, with case-studies of applications for underwater archeology, and ghost fishing gear retrieval. We first synthesize images from sidescan data, apply geometric and radiometric corrections, then use 2D feature detection algorithms to identify point clouds of descriptive visual microfeatures such as corners and edges in the sonar images. We then apply a clustering algorithm on the feature point clouds to group feature sets into regions of interest, reject false positives, yielding a georeferenced inventory of objects.