Deep Distance Metric Learning For Maritime Vessel Identification

Date

2017-05-18

Author

Gundogdu, Erhan
Solmaz, Berkan
Koç, Aykut
Yucesoy, Veysel
Alatan, Abdullah Aydın

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This paper addresses the problem of maritime vessel identification by exploiting the state-of-the-art techniques of distance metric learning and deep convolutional neural networks since vessels are the key constituents of marine surveillance. In order to increase the performance of visual vessel identification, we propose a joint learning framework which considers a classification and a distance metric learning cost function. The proposed method utilizes the quadruplet samples from a diverse image dataset to learn the ranking of the distances for hierarchical levels of labeling. The proposed method performs favorably well for vessel identification task against the conventional use of neuron activations towards the final layers of the classification networks. The proposed method achieves 60 percent vessel identification accuracy for 3965 different vessels without sacrificing vessel type classification accuracy.

Subject Keywords

Vessel identification, Distance metric learning, Deep learning

URI

https://hdl.handle.net/11511/52662

Collections

Graduate School of Natural and Applied Sciences, Conference / Seminar

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Citation Formats

E. Gundogdu, B. Solmaz, A. Koç, V. Yucesoy, and A. A. Alatan, “Deep Distance Metric Learning For Maritime Vessel Identification,” 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52662.