Deep Convolutional Neural Network based Ship Images Classification

Narendra Kumar Mishra; Ashok Kumar; Kishor Choudhury

doi:10.14429/dsj.71.16236

Narendra Kumar Mishra Weapons and Electronics Systems Engineering Establishment, New Delhi - 110 066 https://orcid.org/0000-0003-3735-8906
Ashok Kumar Weapons and Electronics Systems Engineering Establishment, New Delhi - 110 066 https://orcid.org/0000-0002-7672-8693
Kishor Choudhury Weapons and Electronics Systems Engineering Establishment, New Delhi - 110 066 https://orcid.org/0000-0002-4905-8639

DOI: https://doi.org/10.14429/dsj.71.16236

Keywords: Ship classification, Convolutional neural network, VGG16, Transfer learning

Abstract

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.