Traditionally, autonomous underwater vehicles employ multiple configurations of
sensor payloads in order to accomplish a specific mission. Due to advances in imaging
technology, imaging sonar arrays and optical imaging devices are among these payloads.
Independent of mission specifics, the majority of imaging data is either stored onboard
the vehicle or transmitted to a base station for later analysis. In either situation, there
is limited local real time analysis and limited mission duration. One focus for increasing
real time analysis is the reduction of image information. By using image processing
techniques, such as edge detection, less relevant information can be eliminated while preserving
important object features. This reduced object information is then used as inputs
to a neural network. A neural network is a cognitive algorithm which has the ability to
adapt to achieve desired tasks. These networks are able to generalize and make decisions
based on partial or limited input information. The goal of this research is to create an autonomous
in-situ recognition system for marine environments, specifically the processing
and classification of object image data. Image information will be applied to a neural network
approach to mimic higher order decision making in an artificial cognitive algorithm
