Ships are an integral part of maritime traffic where they play both militaries as well as non-combatant roles. This vast maritime traffic needs to be managed and monitored by identifying and recognising vessels to ensure the maritime safety and security. As an approach to find an automated and efficient solution, a deep learning model exploiting convolutional neural network (CNN) as a basic building block, has been proposed in this paper. CNN has been predominantly used in image recognition due to its automatic high-level features extraction capabilities and exceptional performance. We have used transfer learning approach using pre-trained CNNs based on VGG16 architecture to develop an algorithm that performs the different ship types classification. This paper adopts data augmentation and fine-tuning to further improve and optimize the baseline VGG16 model. The proposed model attains an average classification accuracy of 97.08% compared to the average classification accuracy of 88.54% obtained from the baseline model.

A typical warship consists of weapons and sensors that are of diverse origins and are generally based on different design standards and philosophies. But to enhance the operation capability of the platform, it is very important that many of the equipment work in tandem. This paper discusses the design of an integration unit that integrates various sensors and equipment onboard a naval platform. It takes a strictly modular approach and is therefore, adaptable to any size and mission requirement. The proposed solution uses commercial off-the-shelf (COTS) hardware and relevant software, to provide the required Quality of Service (QoS) data to the end equipment and systems. This solution provides an efficient and seamless integration of various sensors, weapons and equipment onboard a naval platform.Keywords: Systems integration, weapons and sensors, data communication