Deep Learning Methods for Ship Classification: From Visible to Infrared Images

Deep learning methods have achieved excellent performances on visual tasks of target recognition and classification. The rapid development of autonomous seafaring vessels comes up with the requirement to recognize other maritime ships day and night. However, the recognition of ships based on the dee...

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Bibliographic Details
Published in2023 5th International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI) pp. 1116 - 1120
Main Authors Liu, Tianci, Qin, Hengjia, Zhan, Zhuo, Liu, Yunpeng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2023
Subjects
Convolutional neural networks
Deep learning
Image recognition
infrared images
Marine vehicles
neural network
Ship recognition
Target recognition
Visualization
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Summary:Deep learning methods have achieved excellent performances on visual tasks of target recognition and classification. The rapid development of autonomous seafaring vessels comes up with the requirement to recognize other maritime ships day and night. However, the recognition of ships based on the deep neural networks may not always access the results as expectation when the ships are under the nighttime environment. To this issue, we consider the ship recognition task under the deep learning framework with paired visible images and infrared images. In this article, we propose an end-to-end convolutional network based on visible images and infrared images of the autonomous seafaring vessels. To demonstrate the effectiveness of our model, we choose the V AIS dataset to test the performance of classifying the maritime ships. Experimental results show that the proposed network outperforms the state-of-the-art methods based on the V AIS database.
DOI:10.1109/RICAI60863.2023.10489039