A Multi-Layer Perceptron Model for Underwater Object Recognition

Abstract

The more human gets interested in sea exploration, the more research to detect objects under the water is done. Therefore, the ability to detect, classify, recognize and track all kinds of objects is evolving daily. This paper aims to introduce a computer model for underwater object classification and recognition based on a Multilayer Perceptron network. The model was constructed with a mixed dataset for the training phase, combining artificial and natural objects, and it reached approximately 99.97% classifying accuracy.