The presented work is an extension of previous work carried out at A.U.G. Signals Ltd. The problem is approached herein for vessel identification/verification using Deep Learning Neural Networks in a persistent surveillance scenario. Using images with vessels in the scene, Deep Learning Neural Networks were set up to detect vessels from still imagery (visible wavelength). Different neural network designs were implemented for vessel detection and compared based on learning performance (speed and demanded training sets) and estimation accuracy. Unique features from these designs were taken to create an optimized solution. This paper presents a comparison of the deep learning approaches implemented and their relative capabilities in vessel verification.
Yi Zang, Abir Mukherjee, Chuhong Fei, Ting Liu, and George Lampropoulos, "Deep learning for anomaly detection in maritime vessels using AIS-cued camera imagery," Proc. SPIE 10190, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VIII, 101900G (Presented at SPIE Defense + Security: April 11, 2017; Published: 4 May 2017); https://doi.org/10.1117/12.2263190.
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