engleski

Marine Objects Recognition Using Convolutional Neural Networks

One of the challenges of maritime affairs is automatic object recognition from aerial imagery. This can be achieved by utilizing a Convolutional Neural Network (CNN) based algorithm. For purposes of this research, a dataset of 5608 marine object images is collected by using Google satellite imagery and Google Image Search. The dataset is divided into two main classes (“Vessels” and “Other objects”) and each class is divided into four sub-classes (“Vessels” sub- classes are “Cargo ships”, “Cruise ships”, “War ships” and “Boats”, while “Other objects” sub- classes are “Waves”, “Marine animals”, “Garbage patches” and “Oil spills”). For recognition of marine objects, an algorithm constructed with three CNNs is proposed. The first CNN for classification on the main classes achieves accuracy of 92.37 %. The CNN used for vessels recognition achieves accuracies of 94.12 % for cargo ships recognition, 98.82 % for cruise ships recognition, 97.64 % for warships recognition and 95.29 % for boat recognition. The CNN used for recognition of other objects achieves accuracies of 88.56 % for waves and marine animals recognition, 96.92 % for garbage patches recognition and 89.21 % for oil spills recognition. This research has shown that CNN is appropriate artificial intelligence (AI) method for marine object recognition from aerial imagery.

artificial intelligence ; Convolutional Neural Network ; marine object recognition ; vessels ; pollution

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