Ship Classification Using Deep Learning Techniques for Maritime Target Tracking
Maxime Leclerc, Ratnasingham Tharmarasa, Mihai Cristian Florea, Anne-Claire Boury-Brisset, Thiagalingam T. Kirubarajan, Nicolas Duclos-Hindie · 2018
In the last five years, the state-of-the-art in computer vision has improved greatly thanks to an increased use of deep convolutional neural networks (CNNs), advances in graphical processing unit (GPU) acceleration and the availability of large labelled datasets such as ImageNet. Obtaining datasets as comprehensively labelled as ImageNet for ship classification remains a challenge. As a result, we experiment with pre-trained CNNs based on the Inception and ResNet architectures to perform ship classification. Instead of training a CNN using random parameter initialization, we use transfer learning. We fine-tune pre-trained CNNs to perform maritime vessel image classification on a limited ship image dataset. We achieve a significant improvement in classification accuracy compared to the previous state-of-the-art results for the Maritime Vessel (Marvel) dataset.