Document Type

Journal Article

Publication Title

Computers, Materials and Continua

Volume

73

Issue

1

First Page

1527

Last Page

1539

Publisher

Tech Science Press

School

School of Engineering

Funders

National Natural Science Foundation of China (61806013, 61876010, 62176009, and 61906005) / General project of Science and Technology Plan of Beijing Municipal Education Commission (KM202110005028) / Beijing Municipal Education Commission Project (KZ201910005008) / Project of Interdisciplinary Research Institute of Beijing University of Technology (2021020101) / International Research Cooperation Seed Fund of Beijing University of Technology (2021A01)

Comments

Zhang, Z., Zhang, T., Liu, Z., Zhang, P., Tu, S., Li, Y., & Waqas, M. (2022). Fine-grained ship image recognition based on BCNN with inception and AM-Softmax. CMC-Computers, Materials and Continua, 73(1), 1527-1539. https://doi.org/10.32604/cmc.2022.029297

Abstract

The fine-grained ship image recognition task aims to identify various classes of ships. However, small inter-class, large intra-class differences between ships, and lacking of training samples are the reasons that make the task difficult. Therefore, to enhance the accuracy of the fine-grained ship image recognition, we design a fine-grained ship image recognition network based on bilinear convolutional neural network (BCNN) with Inception and additive margin Softmax (AM-Softmax). This network improves the BCNN in two aspects. Firstly, by introducing Inception branches to the BCNN network, it is helpful to enhance the ability of extracting comprehensive features from ships. Secondly, by adding margin values to the decision boundary, the AM-Softmax function can better extend the inter-class differences and reduce the intra-class differences. In addition, as there are few publicly available datasets for fine-grained ship image recognition, we construct a Ship-43 dataset containing 47,300 ship images belonging to 43 categories. Experimental results on the constructed Ship-43 dataset demonstrate that our method can effectively improve the accuracy of ship image recognition, which is 4.08% higher than the BCNN model. Moreover, comparison results on the other three public fine-grained datasets (Cub, Cars, and Aircraft) further validate the effectiveness of the proposed method.

DOI

10.32604/cmc.2022.029297

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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