Fine-tuning convolutional neural networks for maritime vessel classification, verification and recognition

Abstract

Autonomous maritime vessel surveillance systems have enormous implications to national defense and global supply chain. Therefore, ship detection and classification problems have been widely studied for a long time. Most of the studies have used satellite imagery, the real-time satellite imaging access is not public and image resolutions is insufficient for high-quality classification and recognition systems. As an alternative approach, consumer-level surveillance cameras have attracted great attention recently due to its cost-effectiveness and easy installation process. Recently, deep learning has become the state-of-the-art method in computer vision field. Deep network architectures have emerged by surpassing human-level accuracy on image classification problems. Many old but powerful ideas have been revised and applied to these networks in various computer vision problems. However, the applications of deep learning methods in the analysis of maritime vessel images are limited in the literature. In this thesis, we employ the state-of-the-art deep network architectures for maritime vessel classification, verification and recognition problems. In the experiments, the most popular three convolutional neural network architectures; AlexNet, VGGNet and ResNet are used. MARVEL dataset is utilized for benchmark purposes, which contains 2M ship images. Since these networks are very difficult to train and they require lots of training samples, we follow transfer learning approach. The main contribution of this thesis is the implementation, tuning and evaluation of specific applications for maritime vessels domain. For classification task, we conduct experiments on different transfer learning techniques and we investigate their performance by transferring the weights layer by layer. We reach the state-of-the-art results by fine-tuning VGG-16 architecture. For both verification and recognition tasks, we use triplet loss heavily inspired by recent advances in the field of face verification and recognition. We achieve closely comparable state-of-the-art results on MARVEL’s both verification and recognition benchmarks.