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

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.

[1]  Henrik Legind Larsen,et al.  Generalized conjunction/disjunction , 2007, Int. J. Approx. Reason..

[2]  Cyril Ray,et al.  Heterogeneous integrated dataset for Maritime Intelligence, surveillance, and reconnaissance , 2019, Data in brief.

[3]  Guillaume Hajduch,et al.  A Multi-Task Deep Learning Architecture for Maritime Surveillance Using AIS Data Streams , 2018, 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA).

[4]  Dimitrios I. Fotiadis,et al.  Automated Diagnosis of Coronary Artery Disease Based on Data Mining and Fuzzy Modeling , 2008, IEEE Transactions on Information Technology in Biomedicine.

[5]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Sattar Hashemi,et al.  Adapted One-versus-All Decision Trees for Data Stream Classification , 2009, IEEE Transactions on Knowledge and Data Engineering.

[7]  Krištof Oštir,et al.  Vessel detection and classification from spaceborne optical images: A literature survey , 2018, Remote sensing of environment.

[8]  Marco Gori,et al.  Integrating Learning and Reasoning with Deep Logic Models , 2019, ECML/PKDD.

[9]  Sergio L. Netto,et al.  A Survey on Performance Metrics for Object-Detection Algorithms , 2020, 2020 International Conference on Systems, Signals and Image Processing (IWSSIP).

[10]  Yoshua Bengio,et al.  A Recurrent Latent Variable Model for Sequential Data , 2015, NIPS.

[11]  Jason Weston,et al.  A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.

[12]  Luca Antiga,et al.  Automatic differentiation in PyTorch , 2017 .

[13]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Ajith Abraham,et al.  Rule-Based Expert Systems , 2005 .

[15]  Alex John London,et al.  Artificial Intelligence and Black-Box Medical Decisions: Accuracy versus Explainability. , 2019, The Hastings Center report.

[16]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[17]  Eric P. Xing,et al.  Harnessing Deep Neural Networks with Logic Rules , 2016, ACL.

[18]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[20]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..