Ship Detection Approach Using Machine Learning Algorithms
Apr 04

Ship Detection Approach Using Machine Learning Algorithms

The control of territorial waters is critical since water occupies more than 70% of the earth’s surface. Due to that fact, maritime security and safety are essential to reduce illegal operations, including piracy, illegal fishing, and transportation of illicit goods. With the rapid development of artificial intelligence, ship detection research has also increased. Several researchers have addressed this issue by proposing various solutions, such as VGG and Dense Net. Nevertheless, these proposed solutions have not provided enough accuracy in ship detection. Therefore, the primary objective of this work is to propose a robust model that can detect ships by applying artificial intelligence and machine learning models, which are Random Forest, Decision Tree, Naive Bayes, and CNN.

The result achieved in this experiment will tackle the aforementioned problems and research how ships could be detected. Based on the result, Random Forest outperforms other models in terms of accuracy, scoring 97.20% for RGB and 98.90% for HSV, in comparison with Decision Tree and Naive Bayes those are scored 96.82% for RGB and 97.18% for HSV and 92.43 for RGB and 96.30% for HSV respectively. Meanwhile, CNN scored 90.45% for RGB and 98.45% for
HSV. Overall, Random Forest is the best model, achieving a good result in RGB and HSV at 97.20% and 98.90%, respectively. The significance of the proposed method for the field of artificial intelligence is to introduce a novel
method to detect Ships.

Article by Abdirahman Osman Hashi1(B), Ibrahim Hassan Hussein1, Octavio Ernesto Romo Rodriguez2, Abdullahi Ahmed Abdirahman1, and Mohamed Abdirahman Elmi1. Read the paper here.

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