Maritime vessel classification based on a dual network combining EfficientNet with a hybrid network MPANet

Autor: Wenhui Liu, Yulong Qiao, Yue Zhao, Zhengyi Xing, Hengxiang He
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: IET Image Processing, Vol 18, Iss 11, Pp 3093-3107 (2024)
Druh dokumentu: article
ISSN: 1751-9667
1751-9659
DOI: 10.1049/ipr2.13158
Popis: Abstract Ship classification is an important technique for enhancing maritime management and security. Visible and infrared sensors are generally employed to deal with the challenging problem and improve classification performance. Herein, a two‐branch feature fusion neural network structure is proposed to classify the visible and infrared maritime vessel images simultaneously. Specifically, in this two‐branch neural network, one branch is based on a deep convolutional neural network that is used to extract the visible image features, while the other is a hybrid network structure that is a multi‐scale patch embedding network called MPANet. The sub‐network MPANet can extract fine‐ and coarse‐grained features, in which the pooling operation instead of the multi‐head attention mechanism is utilized to reduce memory consumption. When there are infrared images, it is used to extract the infrared image features, otherwise, this branch is also utilized to extract visible image features. Therefore, this dual network is suitable with or without infrared images. The experimental results on the visible and infrared spectrums (VAIS) dataset demonstrate that the introduced network achieves state‐of‐the‐art ship classification performance on visible images and paired visible and infrared ship images.
Databáze: Directory of Open Access Journals
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