Automated Curvilinear Mineline Detection
AREA(S): Battlespace, Electronics, Sensors
PROGRAM: Coastal Battlefield Reconnaissance and Analysis (COBRA), PMS 495, Mine
Warfare Program Office
technology within this topic is restricted under the International Traffic in
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technical data, or the Export Administration Regulation (EAR), 15 CFR Parts
730-774, which controls dual use items. Offerors must disclose any proposed use
of foreign nationals (FNs), their country(ies) of origin, the type of visa or
work permit possessed, and the statement of work (SOW) tasks intended for
accomplishment by the FN(s) in accordance with section 3.5 of the Announcement.
Offerors are advised foreign nationals proposed to perform on this topic may be
restricted due to the technical data under US Export Control Laws.
Develop an automated curvilinear mineline detection algorithm.
The Navy is interested in technologies that facilitate automated target pattern
recognition capabilities in aerial multi-spectral images of
curvilinear-arranged targets in various coastal and inland environments. COBRA
multi-spectral imagery has wavelengths in the visible spectrum, including those
that penetrate the water, and infrared. These minefields may be placed in a
variety of configurations. Current patterned algorithms exploit a mineline’s
linear features and rely on minimal variations in mine-to-mine placement angles
to make a detection. This tradeoff improves the algorithm’s linear mineline
performance. Automated target recognition algorithms that annotate minefields
placed in nonlinear patterns would reduce mission execution time during the
post mission analysis phase and improve detection system performance.
Typically, minelines placed in a coastal environment follow the natural
landforms of the area and may take on complex, non-linear shapes. Accurate and
reliable automatic detection and notification of the presence of these curvilinear
minelines would reduce operator review time to mark the area for clearance or
avoidance by follow-on forces. Studies have shown that an accurate and reliable
automatic detection algorithm reduced detection time and improved detection
rate. If all of the algorithm’s cues are false alarms, operator performance may
be worse than if no aiding was provided at all. This would reduce the mission
time and the potential for error due to operator fatigue and human error.
The Navy needs innovative methods that can recognize non-linear, patterned
targets in a variety of inland and coastal environments as imaged aerially with
a multi-spectral camera. The proposed effort will develop algorithms for
automated target recognition of curvilinear minelines to optimize Probability
of Detection (PD) and Probability of False Alarm (PFA)/False Alarm Rate (FAR)
of the COBRA Block I System. Targets will have a top surface area equivalent to
that of a circle with a diameter of approximately 15 to 30 centimeters, which
equates to approximately 6-14 pixels in COBRA’s imagery. In order to work
within the current COBRA Block I Real Time Processor (RTP) framework, the
algorithms will need to be modular as the RTP uses independent algorithm
libraries. Modules will perform logically discrete functions and provide
well-defined interfaces for other modules. Algorithms will be hardware
agnostic, but for development considerations only, will run on an Intel-based
64-bit architecture system with discrete NVIDIA graphics cards. As newer hardware
becomes available, the algorithm kernels should be capable of scaling to
utilize available resources. These modular algorithms will be integrated into
the COBRA Airborne Payload Subsystem (CAPS), the COBRA Post Mission Analysis
(PMA) Subsystem, and potentially other flight and post-mission analysis systems
as identified. The algorithms will be implemented as object-oriented C++ for
Central Processing Units (CPUs) and/or Open Computing Language (OpenCL) or
Compute Unified Device Architecture (CUDA) for Graphics Processing Unit (GPU)
processing. Processing techniques should work in conjunction with the current
RTP framework and algorithms to process imagery in real time; currently an
image to be processed is captured every 763 milliseconds. The proposed algorithms
will be required to conform to the Navy’s Open Architecture (OA) initiative.
Modular design of software components will enable openness to the Navy and
The Phase II effort will likely require secure access, and NAVSEA will process
the DD254 to support the contractor for personnel and facility certification
for secure access. The Phase I effort will not require access to classified
information. If need be, data of the same level of complexity as secured data
will be provided to support Phase I work.
Work produced in Phase II may become classified. Note: The prospective
contractor(s) must be U.S. Owned and Operated with no Foreign Influence as
defined by DoD 5220.22-M, National Industrial Security Program Operating
Manual, unless acceptable mitigating procedures can and have been be
implemented and approved by the Defense Security Service (DSS). The selected
contractor and/or subcontractor must be able to acquire and maintain a secret
level facility and Personnel Security Clearances, in order to perform on
advanced phases of this contract as set forth by DSS and NAVSEA in order to
gain access to classified information pertaining to the national defense of the
United States and its allies; this will be an inherent requirement. The
selected company will be required to safeguard classified material IAW DoD
5220.22-M during the advance phases of this contract.
I: Develop a concept for an algorithm capable of detecting curvilinear
minelines in a variety of inland and coastal environments using aerial
multispectral imagery. Demonstrate the feasibility of the algorithm through
modeling and simulation. Develop a Phase II plan. The Phase I Option, if
exercised, will include the initial design specifications and capabilities
description to build a prototype solution in Phase II.
II: Develop and deliver a modular software library prototype to provide
efficient real-time detection of curvilinear minelines using COBRA Block I
imagery as described in the Description. The prototype may run in a development
environment that meets the hardware performance specifications and software
libraries of the COBRA Block I RTP. Generate a performance estimation of the
developed capability to include PD, PFA/FAR, operating time, and operational
impacts of environmental conditions including clutter and vegetation. Use
operationally representative data for the evaluation. Ensure that the algorithm
performance meets the system’s minefield detection performance using specified
target sizes. Prepare a Phase III development plan to transition the technology
for Navy and potential commercial use.
It is probable that the work under this effort will be classified under Phase
II (see Description section for details).
III DUAL USE APPLICATIONS: Support the Navy in transitioning the technology for
Navy use. While algorithm modularity eases integration, integrate the
algorithms into the RTP. Perform the following integration tasks: adding the
algorithms into the existing processing framework, load balancing across the RTP’s
various processors, and acceptance testing in the operational configuration.
Further refine the software to ensure compatibility with existing mine warfare
operator interfaces and workstations according to the Phase III SOW. Support
updates to the COBRA Technical Data Package to support the Navy in
transitioning the design and technology into the COBRA Production baseline for
future Navy use.
The technology developed here can be applied to pattern recognition problems,
surveillance tasks, remote sensing, and Intelligence Preparation of the
Operational Environment (IPOE). Commercial applications include biometrics,
computer vision, facial recognition, and histopathology.
"AN/DVS-1 Coastal Battlefield Reconnaissance and Analysis (COBRA)."
The U.S. Navy – Fact File. Last update 4 October 2017. http://www.navy.mil/navydata/fact_display.asp?cid=2100&tid=1237&ct=2
Bernabe, Sergio, Lopez, Sebastian, Plaza, Antonio, and Sarmiento, Roberto. “GPU
Implementation of an Automatic Target Detection and Classification Algorithm
for Hyperspectral Image Analysis.” IEEE Geoscience and Remote Sensing Letters,
Vol. 10, No. 2, March 2013. https://ieeexplore.ieee.org/abstract/document/6218752/
Reiner, Adam J., Hollands, Justin G., and Jamieson, Greg A. “Target Detection
and Identification Performance Using an Automatic Target Detection System.”
Human Factors, Vol. 59, No. 2, 01 March 2017, pp. 242-258. https://doi.org/10.1177/0018720816670768
Samson, Joseph W., Witter, Lester J., Kenton, Arthur C., and Holloway, John H.
“Real-time Implementation of a Multispectral Target Detection Algorithm.” SPIE
5089, Detection and Remediation Technologies for Mines and Minelike Targets
VIII, 11 September 2003. https://doi.org/10.1117/12.501567
El-Saba, Aed, Alam, Mohammad S., and Sakla, Wesam A. “Pattern Recognition via
Multispectral, Hyperspectral, and Polarization-based Imaging.” SPIE Defense,
Security and Sensing, Proceedings Volume 7696, Automatic Target Recognition XX;
Acquisition Tracking, Pointing, and Laser System Technologies XXIV; and Optical
Pattern Recognition XXI, 13 May 2010. https://doi.org/10.1117/12.851689
Automated Target Detection; Automated Pattern Detection; Curvilinear Minefields;
Coastal Battlefield Reconnaissance and Analysis; COBRA; Post Mission Analysis;
Mine Countermeasures; MCM
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