Maritime Border Security using Sensors, Processing, and Platforms to Detect Dark Vessels

Maritime security is critical to national prosperity, but with a large area to be secured and limited resources available, our current maritime situational awareness is not sufficient to secure our borders. This challenge is compounded by the fact that many bad actors intentionally avoid using transponders to obscure their location and actions for nefarious purposes. New solutions are required to detect these “dark” vessels in the expansive maritime domain and thereby enable increased maritime situational awareness and security. In this paper, we present a concept for increased maritime situational awareness, specifically designed to secure our borders against dark vessels using a novel combination of existing technologies. Our concept is Sensors and Platforms for Unmanned Detection of Dark Ships (SPUDDS), which consists of our intelligent ship detection and classification software integrated onboard our autonomous long-duration sensor buoy, which provides long-range passive detection of non-emitting dark vessels for maritime situational awareness.

[1]  Camille Monnier,et al.  A Multi-scale Boosted Detector for Efficient and Robust Gesture Recognition , 2014, ECCV Workshops.

[2]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[3]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  M. Pietikäinen,et al.  TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS , 2004 .

[5]  Vincent Lepetit,et al.  Fast Keypoint Recognition Using Random Ferns , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[7]  Vincent Lepetit,et al.  BRIEF: Binary Robust Independent Elementary Features , 2010, ECCV.