Maritime vessel images classification using deep convolutional neural networks

Cuong Duc Dao, Hua Xiaohui, Olivier Morère

Abstract: The ability to identify maritime vessels and their type is an important component of modern maritime safety and security. In this work, we present the application of deep convolutional neural networks to the classification of maritime vessel images. We use the AlexNet deep convolutional neural network as our base model and propose a new model that is twice smaller then the AlexNet. We conduct experiments on different configurations of the model on commodity hardware. We comparatively evaluate and analyse the performance of different configurations the model. We measure the top-1 and top-5 accuracy rates. The contribution of this work is the implementation, tuning and evaluation of automatic image classifier for the specific domain of maritime vessels with deep convolutional neural networks under the constraints imposed by commodity hardware and size of the image collection.