Ship images detection and classification based on convolutional neural network with multiple feature regions

Abstract

In recent years, the maritime industry is developing rapidly, which poses great challenges for intelligent ship navigation systems to achieve accurate ship classification. To cope with this problem, a Recurrent Attention Convolutional Neural Network (RA-CNN) is proposed, which is fused with multiple feature regions for ship classification. The proposed model has three scale layers, each of which contains a classification network VGG-19 and a localisation head Attention Proposal Network (APN). First, the Scale Dependent Pooling algorithm is integrated with VGG-19 to reduce the impact of over-pooling and improve the classification performance of small ships. Second, the APN incorporates the Joint Clustering algorithm to generate multiple independent feature regions; thus, the whole model can make full use of the global information in ship recognition. In the meantime, the Feature Regions Optimisation method is designed to solve the overfitting problem and reduce the overlap rate of multiple feature regions. Finally, a novel loss function is defined to cross-train VGG-19 and APN, which accelerates the convergence process. The experimental results show that the classification accuracy of the authors’ proposed method reaches 90.2%, which has a 6% improvement over the baseline RA-CNN. Both classification accuracy and robustness are improved by a large margin compared to those of other compared models.