Pitsikalis, Manolis, Thanh-Toan, Do, Lisitsa, Alexei and Luo, Shan 
ORCID: 0000-0003-4760-0372
(2021)
Logic Rules Meet Deep Learning: A Novel Approach for Ship Type Classification.
In: 5th International Joint Conference on Rules and Reasoning, 2021-9-13 - 2021-9-15, Leuven, Belgium.
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Ship_Type_Classification_Using_Vessel_Images_and_AIS_Data___RULEML_RR.pdf - Author Accepted Manuscript Download (699kB) | Preview |
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.
| Item Type: | Conference or Workshop Item (Unspecified) |
|---|---|
| Additional Information: | Accepted and presented in RuleML+RR 2021 |
| Uncontrolled Keywords: | Object detection, Classification rules, Fuzzy rules |
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 09 Aug 2021 10:50 |
| Last Modified: | 08 Mar 2023 21:51 |
| DOI: | 10.1007/978-3-030-91167-6_14 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3132906 |
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