Machine Learning for Ship Vessel Classifications Augmented with Synthetic Images

Haze conditions have reportedly reduced visibility to about 3km in some of the busiest shipping lanes in the world. Haze conditions, including inclement weather conditions, are identified as a key challenge for autonomous vehicle operations. However, field data on poor weather conditions and ship images under hazy conditions may not be readily available to support research work aimed toward overcoming such challenges for autonomous vehicles. In this thesis, synthetic ship images are rendered under hazy conditions to augment a baseline dataset of haze-free ship images, in order to support our research on ship vessel classifications in a hazy environment using machine learning. The proposed feature extraction involves the counting of corner points detected using the Kanade Lucas Tomasi (KLT) technique to characterize the pattern of specific ship classes and computing of higher-order moments on the color planes on the ship structure detected in the images. Results show that the average ship classification accuracy rate is about 40% higher when the model is trained using a dataset augmented with synthetic hazy ship images; the classifier can classify for ship classes such as container ships, cargo ships, and sailing vessels, with an 80% average accuracy rate.