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Proceedings Paper

Marine ship recognition based on cascade CNNs
Author(s): Huarong Jia; Liang Ni
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Paper Abstract

Marine ship recognition is a challenging Fine-Grained Visual Categorization (FGVC) problem due to the large visual variations caused by motion blur, occlusion, lighting changes, and etc. The visual distinction between similar categories is usually very small, so it is difficult to solve it with general recognition algorithm. It demands an advanced discriminative model to accurately segment marine ships from the backgrounds and classify the type of the ship. However, effective models for the problem tend to be computationally prohibitive. To address these two conflicting challenges, we propose to recognize marine ship based on two cascade CNNs (convolutional neural networks), a shallow CNN and a deep CNN. The shallow CNN is used to quickly remove most of the background regions to reduce the computation cost, and the deep CNN is used to classify the type of ship in the remaining regions. The two CNNs are trained end-to-end, and they are complementary to each other to guarantee the recognition precision with low computation cost. Experimental results show that the proposed method is promising for marine ship recognition.

Paper Details

Date Published: 31 January 2020
PDF: 6 pages
Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114270A (31 January 2020); doi: 10.1117/12.2549147
Show Author Affiliations
Huarong Jia, Beijing Institute of Control and Electronic Technology (China)
Liang Ni, Beijing Institute of Control and Electronic Technology (China)

Published in SPIE Proceedings Vol. 11427:
Second Target Recognition and Artificial Intelligence Summit Forum
Tianran Wang; Tianyou Chai; Huitao Fan; Qifeng Yu, Editor(s)

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