Autonomous ship classification using synthetic and real color images

Abstract

This work classifies color images of ships attained using cameras mounted on ships and in harbors. Our data-sets contain 9 different types of ship with 18 different perspectives for our training set, development set and testing set. The training data-set contains modeled synthetic images; development and testing data-sets contain real images. The database of real images was gathered from the internet, and 3D models for synthetic images were imported from Google 3D Warehouse. A key goal in this work is to use synthetic images to increase overall classification accuracy. We present a novel approach for autonomous segmentation and feature extraction for this problem. Support vector machine is used for multi-class classification. This work reports three experimental results for multi-class ship classification problem. First experiment trains on a synthetic image data-set and tests on a real image data-set, and obtained accuracy is 87.8%. Second experiment trains on a real image data-set and tests on a separate real image data-set, and obtained accuracy is 87.8%. Last experiment trains on real + synthetic image data-sets (combined data-set) and tests on a separate real image data-set, and obtained accuracy is 93.3%.