Neural networks for sidescan sonar automatic target detection

Abstract

The goal of this research is to develop a multi-layer feedforward neural network architecture which can distinguish targets (in this case, mines) from background clutter in sidescan sonar images. The network is to be implemented on a hardware neurocomputer currently in development at CSDL, with the goal of eventual real-time performance in the field. A variety of neural network architectures are developed, simulated, and evaluated in an attempt to find the best approach for this particular application. It has been found that classical statistical feature extraction is outperformed by a much less computationally expensive approach that simultaneously compresses and filters the raw data by taking a simple mean.<>