Visual Detection of Maritime Vessels

Abstract

This thesis investigates how a tailored Convolutional Neural Network (CNN) can aid autonomous surface vehicles (ASVs) in detecting and classifying maritime traffic for collision avoidance. Several state of the art CNN models are presented and trained on data sets with relevance to the above-mentioned objective. Data collected from different sources are used for training these CNN models in pursuit to obtain a good performing detector. The main data sets are large, general purpose image sets of ships and boats. A smaller image set is also developed in this thesis. This custom data set is constructed from images taken along a predefined path at sea from a video camera. This includes images along docks and of ships in transit at sea. This data set is then split into training and testing images which are in close relation to each other. Through experiments, the various data sets are used to train both a 5 layer deep and a 16 layer deep CNN model to detect ships in an image.