Open Access

ARTICLE

Fine-grained Ship Image Recognition Based on BCNN with Inception and AM-Softmax

Zhilin Zhang¹, Ting Zhang¹, Zhaoying Liu^1,*, Peijie Zhang¹, Shanshan Tu¹, Yujian Li², Muhammad Waqas³

1 Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2 School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, 541004, China
3 School of Engineering, Edith Cowan University, Perth WA 6027, Australia

* Corresponding Author: Zhaoying Liu. Email:

Computers, Materials & Continua 2022, 73(1), 1527-1539. https://doi.org/10.32604/cmc.2022.029297

Received 01 March 2022; Accepted 01 April 2022; Issue published 18 May 2022

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.

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Keywords

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