Logic Rules Meet Deep Learning: A Novel Approach for Ship Type Classification

TL;DR

This paper introduces a novel ship type classification model that combines deep learning and rule-based systems, improving accuracy and explainability by integrating vessel data and imagery.

Contribution

The paper presents a new hybrid approach combining Faster R-CNN and Neuro-Fuzzy systems for ship classification, enhancing accuracy and interpretability over existing methods.

Findings

Prediction scores increased by up to 15.4%

Model maintains explainability compared to black box approaches

Demonstrates effectiveness on real-world data

Abstract

The shipping industry is an important component of the global trade and economy, however in order to ensure law compliance and safety it needs to be monitored. In this paper, we present a novel Ship Type classification model that combines vessel transmitted data from the Automatic Identification System, with vessel imagery. The main components of our approach are the Faster R-CNN Deep Neural Network and a Neuro-Fuzzy system with IF-THEN rules. We evaluate our model using real world data and showcase the advantages of this combination while also compare it with other methods. Results show that our model can increase prediction scores by up to 15.4\% when compared with the next best model we considered, while also maintaining a level of explainability as opposed to common black box approaches.